BEQ_report_WP5_090823 by xiagong0815

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									           Description of
European Prototype Tool for Evaluation
      of Building Performance
       and the national tools




                           Editors:
          Prof. Livio Mazzarella, Michele Liziero
                    Politecnico di Milano
                         Milan, Italy

             Christian Neumann, Dirk Jacob
       Fraunhofer Institute for Solar Energy Systems
                    Freiburg, Germany
                                                                     Building EQ – Tool Description




                            Building EQ
Tools and methods for linking EPDB and continuous commissioning
             Is supported by the European Commission in the programme
                           Intelligent Energy – Europe (IEE)



                                    Key Action: SAVE

                         Agreement N° : EIE/06/038/SI2 .448300



      This report was prepared as a deliverable of workpackage 5 of Building EQ
                                      May 2009



                             For more information visit us at:

                                   www.buildingeq.eu




                                        Disclaimer

    The sole responsibility for the content of this report lies with the authors. It does
    not necessarily reflect the opinion of the European Communities. The European
    Commission is not responsible for any use that may be made of the information
                                     contained therein.




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                                                                                                       Building EQ – Tool Description




Table of contents

1    Introduction..................................................................................................................1
2    Features of the European tool....................................................................................2
    2.1    Data formats and handling ..................................................................................2
     2.1.1     The minimal data set ..................................................................................................... 3
     2.1.2     Unified point naming convention.................................................................................... 4
     2.1.3     Measured data format (raw data) .................................................................................. 6
     2.1.4     Meta data format............................................................................................................ 7
    2.2    Visualization .......................................................................................................10
     2.2.1     General plot types........................................................................................................ 10
     2.2.2     Energy and water consumption ................................................................................... 13
     2.2.3     Heating and cooling circuits......................................................................................... 21
     2.2.4     Air handling units (AHUs) ............................................................................................ 28
    2.3    Building specific benchmarks........................................................................... 36
     2.3.1     General model structure .............................................................................................. 36
     2.3.2     Zone model.................................................................................................................. 37
     2.3.3     Model for Air Handlin Units (AHUs) ............................................................................. 40
     2.3.4     General model for system components ....................................................................... 41
     2.3.5     Overall system model .................................................................................................. 42
    2.4    Automated outlier detection .............................................................................. 43
3    User Guide .................................................................................................................45
    3.1    Creating a new project .......................................................................................47
     3.1.1     Importing raw data ....................................................................................................... 47
    3.2    Inspecting the data............................................................................................. 49
     3.2.1     Sensorgroup options.................................................................................................... 49
     3.2.2     Sensor options............................................................................................................. 51
    3.3    Importing / editing meta data ............................................................................ 52
     3.3.1     Importing meta data..................................................................................................... 52
     3.3.2     Edit the meta data........................................................................................................ 53
    3.4    Process data .......................................................................................................54
    3.5    Visualizations ..................................................................................................... 55
    3.6    Model based analysis.........................................................................................56
     3.6.1     Day profiles.................................................................................................................. 58
     3.6.2     Schedules .................................................................................................................... 59
     3.6.3     Zones........................................................................................................................... 60
     3.6.4     System components .................................................................................................... 64


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      3.6.5     Air Handling Units (AHUs) ........................................................................................... 68
      3.6.6     Splitter/merger ............................................................................................................. 70
      3.6.7     Cross table view .......................................................................................................... 72
      3.6.8     Invoking the calculation / inspect results ..................................................................... 73
      3.6.9     csv-export of data ........................................................................................................ 75
    3.7     Automated Outlier detection ............................................................................. 75
4     National tools for performance analysis of buildings ............................................ 76
    4.1     German: ennovatis tool ..................................................................................... 76
      4.1.1     Structure of the tools.................................................................................................... 76
      4.1.2     Data handling............................................................................................................... 76
      4.1.3     Data Visualization ........................................................................................................ 77
      4.1.4     Graphical User interface .............................................................................................. 78
      4.1.5     Method of analysis....................................................................................................... 78
      4.1.6     Validation ..................................................................................................................... 79
      4.1.7     Application to Fault Detection and Diagnosis .............................................................. 82
    4.2     Finland: Granlund tool ....................................................................................... 84
      4.2.1     Structure of the tool Taloinfo........................................................................................ 84
      4.2.2     User interface and data visualization........................................................................... 88
Annex ............................................................................................................................... 91
    Unified point naming convention ..................................................................................1
    Background on implementation ....................................................................................6




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                                                                          Building EQ – Tool Description




    1 Introduction
The aim of the project Building EQ is to support the introduction of both: the Energy Performance
of Buildings Directive (Directive 2002/91/EC) and of continuous commissioning 1, which is seen as
a prerequisite for an energy efficient long term operation of buildings.
The Directive prescribes energy certificates for new and existing buildings, while considering the
building envelope and the HVAC systems as parts of the same entity and thereby establishing a
basis for global optimization of building performance.
However, although the principle link is obvious, it has to be stated that both – EPBD and
continuous commissioning - are relatively new approaches that are still under (intensive)
development. The current state of the implementations of the EPBD and the diversity between
countries makes a direct coupling on a common basis difficult.
In the framework of Building EQ, Guidelines for the Evaluation of Building Performance have been
developed. The Guidelines describe a 4-step procedure for the cost effective performance analysis
following a general top-down approach that tries to combine the outcomes of the certification
process according to EPBD with CC. The idea of this top-down approach is to put effort in form of
measurements and analysis only where and when necessary. The transition from one step to the
next should only be performed if certain criteria are fulfilled.
The objective of WP5 (Development of assessment-tools based on EPBD data) is to develop a set
of tools that can support this 4-step procedure. The development has two lines: one is the
enhancement of the existing tools of the industrial Partners (Granlund and ennovatis). The second
line is a tool on European level, which will have a basic functionality.
As the tools of the industry partners are more sophisticated but partially specific to the respective
country the main purpose is to use them as a benchmark for the development of the European
tool.
This report will give an overview over the features of the European tool and a description of the
tools of the industry partners.




1
 Continuous or ongoing commissioning. The term denotes an ongoing process for the quality assurance of
building performance. It is designed to develop targets and to verify and document their achievement.

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                                                                                  Building EQ – Tool Description




    2 Features of the European tool
On the basis of previous results of the Building EQ project, the consortium decided that the
European tool should comprise the following features:
           o    Data handling
                General features for storing time series data.
           o    Data visualization
                “Intelligent” data visualization on the basis of the minimal data set that reveals
                “operation patterns”.
           o    Building specific benchmark (Model based analysis)
                A simplified model of the building (zones + HVAC) is used for identification of faults
                and optimization potentials. Models in this context are based on the regulations of
                the CEN standard as much as reasonable.
           o    Statistical analysis / simple rule based analysis
                Find correlations between the variables of the minimal data set in order to identify
                unusual behaviour or faults in operation, respectively.


The tool itself has a modular structure. The kernel is a data storage library which stores measured
data. The analysis functions and the importer are modules which can be connected to the data
storage.




                                         Data Storage


                                           Library
                                            Core




                 ASCII                                                 Building
               Importer                                                specific
                                                                      benchmark

                              Visuali-                  Statistical
                              zation                     analysis




Figure 1   Schematic of structure of the European tool



The next chapters will give a short description of the content and implementation of the features.

    2.1 Data formats and handling
The features of the tool are all based on measured data of the building performance. This chapter
describes the data, the formats and the data handling involved. A crucial part for the automation of
processes is a unified point naming convention which will also be presented.

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           2.1.1 The minimal data set
In order to evaluate the performance of a building measured data – at least of the energy
consumption – is necessary. Generally, the availability of measured data with sufficient quality in
existing buildings is low. At the same time the monitoring of all components of a system usually
requires a considerable budget for additional measurements and is not feasible. Considering this
situation the analyst has to decide upon a minimal data set that is able to reveal the characteristics
of the performance without demanding to much budget.
In the framework of Building EQ a minimal set of measured data was consciously chosen. It could
either be acquired and recorded by a Building Automation System (BAS) or with a separate data
logger (see report “Guidelines for Evaluation of Building Performance” for details 2). It is believed to
be the minimal amount of measured data that is necessary to facilitate a rough overall assessment
of the performance of the system. This minimal data set is shown in Table 1.

Table 1          Minimal data set for Building EQ


    item              Measured value                         possible     remarks
                                                              units

    consumption       total consumption of fuels           Wh, kWh, MWh   (e.g. gas, oil, biomass)
                                                                          Must be provided as accumulated value

                      total consumption of district heat   Wh, kWh, MWh   Must be provided as accumulated value

                      total consumption of district cold   Wh, kWh, MWh   Must be provided as accumulated value

                      total consumption of electricity     Wh, kWh, MWh   Must be provided as accumulated value

                      total consumption of water               m³, l      Must be provided as accumulated value

    weather           outdoor air temperature                   °C        own weather station or from weather data
                                                                          provider

                      outdoor rel. humidity                     %         own weather station or from weather data
                                                                          provider

                      global irradiation                      W/m²        own weather station or from weather data
                                                                          provider

    indoor            indoor temperature                        °C        choose one or more reference zones for that
    conditions                                                            measurement

                      indoor relative humidity                  %         choose one or more reference zones for that
                                                                          measurement

    system            Flow / return Temperatures of             °C        main heat/cold distribution.
                      main water circuits                                 ”Main” in this context refers to the
                                                                          distribution in the building and not to a
                                                                          primary distribution such as a district heating
                                                                          system.

                      supply and exhaust air                    °C        for major equipment
                      temperature of main AHUs

                      supply and exhaust air relative           %         for major equipment



2
 Download here:
http://www.buildingeq-online.net/fileadmin/user_upload/Results/report_wp3_080229_final.pdf

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 item               Measured value                            possible         remarks
                                                               units
                    humidity of main AHUs

                    control signals of pumps and fans       0-100%, 0-1        for major equipment
                    (if available)                               or
                                                             on/off, 0/1


For the full functionality of the tool – before all the visualization - the minimal data set has to be
available. The values should be recorded with a time resolution of 5-10 minutes as current values
(no averaging over the last measurement interval).

        2.1.2 Unified point naming convention
In order to make the tool easily applicable to many buildings and to have analysis processes
automated, a unified point naming convention is a necessary prerequisite. By this convention the
user is able to “tell” the tool which data points are available and the tool can identify them
automatically.
Furthermore the point naming convention assures that data points have unique names (which the
original names coming e.g. from the BAS might not have in some cases).
The standard point names are composed of predefined abbreviations for hierarchical categories.
The categories start at the building level and go down to the sensor. Table 2 shows the categories.

Table 2        categories for point naming convention


          No      Category            Remark

          1       Building            User defined
                                      Name of the building or abbreviation of name

          2       Zone                User defined
                                      Name of Zone to which sensor corresponds (not the location of the
                                      sensor!). e.g. name of the zone which is served by the AHU that the sensor
                                      belongs to (e.g. supply air temperature)

          3       System              main system to which the sensor belongs
                                      (e.g. air handling unit (AHU), weather station, energy supply)

          4       Subsystem1          if appropriate: subsystem of system
                                      (e.g. heating coil of an AHU or pump of a heating circuit)

          5       Subsystem2          if appropriate: subsystem of subsystem 1
                                      (e.g. pump of heating coil for an AHU)

          6       Medium              if appropriate: medium in which the sensor is placed
                                      (e.g. hot water, chilled water, supply air, etc.)

          7       Position            if appropriate: position of the sensor
                                      (e.g. supply or return)

          8       Kind                kind of the data point
                                      (either: measured value, set value, operation signal, alarm)

          9       Datapoint           The (physical) quantity which is measured
                                      (e.g. temperature, energy, status))




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For every category a list of possible items and corresponding abbreviations were defined. A
complete list for the minimal data set is given in the Annex. If the point names are noted down, the
categories are separated by underscore „_“. Further (potentially user defined) specifications can be
appended after a dot „.“ if needed. If a category is not used (e.g. subsystem2) it stays “empty”, i.e.
it has no value.
Two examples are as follows (please refer to Annex for the abbreviations):
   o   Example 1:
       Measured supply air temperature of AHU 1 in Building A:
           o   Building:      BuildingA
           o   Zone:          -
           o   System:        AHU.1
           o   Subsystem1: -
           o   Subsystem2: -
           o   Medium:        SUPA
           o   Position:      -
           o   Kind:          MEA
           o   Datapoint:     T
           o     Full name:
               Building.A_ _AHU.1_ _ _SUPA_ _MEA_T
   o   Example 2:
       Status of (secondary) Pump of heating coil of AHU 1 in Building A:
           o   Building:      BuildingA
           o   Zone:          -
           o   System:        AHU.1
           o   Subsystem1: HC
           o   Subsystem2: PU
           o   Medium:        HW
           o   Position:      SEC
           o   Kind:          SIG
           o   Datapoint:     STAT
           o     Full name:
               Building.A_ _AHU.1_HC_PU_HW_SEC_SIG_STAT


The analysis routines of the tool use this kind of point names in order to identify necessary data
points for the algorithm.
However, the tool does not require a renaming of all data points before the data is imported. The
categories of the unified point name are specified as meta data (see chapter 2.1.4 and 2.4).




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                                                                           Building EQ – Tool Description




        2.1.3 Measured data format (raw data)
The measured data must be provided in one or more ASCII-files (csv) with predefined format:
    o      1st row:
           header with original data point names from BAS or data logger
    o      1st column:
           Equidistant timestamps (date and time) in predefined format (dd.mm.yyyy HH:MM).
           the time resolution should not be less than 15 Minutes
    o      From 2nd column on:
           data (values of data points at timestamp)
    o      Error values: -999 (or as specified)
    o      The columns are separated by a semicolon “;”


The following figure gives an example of a data file

     DateTime;                   [dp1]; [dp2]; [dp3]; [dp4]
     01.01.09 02:00;             21.2; 100;     22.0; -999
     01.01.09 02:10;             21.3; 103;     22.2; -999
     01.01.09 02:20;             21.4; 110;     21.5; -999
     01.01.09 02:30;             21.7; 130;     20.7; -999
     …                           …        …     …      …
     …                           …        …     …      …
     …                           …        …     …      …
     …                           …        …     …      …



Figure 2       Example of ASCII raw data file

These files are called raw-data and can contain arbitrary periods of time. However, they all must
have the same time resolution.




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                                                                                            Building EQ – Tool Description




        2.1.4 Meta data format
Meta data contain standard meta data for every measured data point like unit and min/max values.
Furthermore it contains the specifications of the categories for the unified point naming convention.
The meta data can be input manually in the tool but it can also be imported from an ASCII (csv)
file. The format of this ASCII file has to follow these conventions:
    o     1st row gives the identifiers of the meta data (see Table 3)
          In order to distinguish standard meta data from meta data for categories the identifiers have
          either the prefix “SM_” or “CAT_”
    o     1st column: data point names as given in raw data file
    o     From 2nd row on, the meta data of the data point in the corresponding row is specified

Table 3        Required meta data
               Identifier and possible items for the minimal data set of Building EQ.


          No    identifier                  description                  possible items for minimal data set

                Standard meta data

          1     SM_sensortype               The type of sensor or             o    accumulated_energy
                                            data point respectively           o    accumulated_volume
                                                                              o    temperature
                                                                              o    linear_signal
                                                                              o    status

          2     SM_unit                     Physical unit.                    o    See Table 1

          3     SM_samplemethod             method which is used for          o    “difference” for accumulated energy and
                                            sampling of hourly data                water
                                                                              o    “average” for all other sensors

          4     SM_min                      minimal value for                 o    User defined
                                            plausibility check of data        o    Note:
                                            point                                  for energy consumption this value will
                                                                                   refer to the corresponding power in kW
                                                                                   and for water consumption to flow
                                                                                   rate in l/h!

          5     SM_max                      maximal value for                 o    User defined
                                            plausibility check of data        o    Note:
                                            point                                  for energy consumption this value will
                                                                                   refer to the corresponding power in kW
                                                                                   and for water consumption to flow
                                                                                   rate in l/h!

          6     SM_description              description of the data           o    user defined text that describes sensor
                                            point                                  (e.g. “AHU1: return temperature of
                                                                                   heating coil”)

                Categories for unified point naming

          1     CAT_building                                             see 2.1.2 for explanation

          2     CAT_zone



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       No   identifier            description            possible items for minimal data set

       3    CAT_system

       4    CAT_subsystem1

       5    CAT_subsystem2
                                                        see 2.1.2 for explanation
       6    CAT_medium

       7    CAT_position

       8    CAT_kind

       9    CAT_point




The figure on the next page gives an example of a meta data file.




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                                                                                                                                                                           Building EQ – Tool Description




      SM_sensortype;         SM_unit;   SM_samplemethod;   SM_min;   SM_max;   SM_description;   CAT_building;   CAT_zone;   CAT_system,   CAT_subsystem1;   CAT_subsystem2;   CAT_medium   CAT_position;   CAT_kind;   CAT_point

DP1   temperature;           C;         average;           -20;      40;       outdoor air T;    BUI1;           ;           WTH;          ;                 ;                 OA           ;               MEA;        T


DP2   accumulated energy;    kWh;       difference;        0;        200;      district heat;    BUI1;           WBD;        DH;           MTR.H;            ;                 HW           ;               MEA;        E.H


DP3   accumulated water;     m³         difference;        0;        2000;     water consump.    BUI1;           WBD;        WSUP;         MTR.W;            ;                 DCW;         ;               MEA;        VOL


DPX   limear_signal;         %;         average;           0;        100;      control P1;       BUI1;           1stFloor;   WC.H.rad;     PU;               ;                 HW;          SUP;            SIG;        CTRLSIG


…     …                      …          …                  …         …         …                 …               …           …             …                 …                 …            …               …           …


…     …                      …          …                  …         …         …                 …               …           …             …                 …                 …            …               …           …




          Figure 3          Example of meta data file.




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   2.2 Visualization

      2.2.1 General plot types
On the basis of the minimal data set predefined visualizations are prepared by the tool. These can
be used for manual fault detection and diagnosis either for an initial analysis or for monitoring.
Principally the following chart types are use:
            o   Time series plot
                Chronological sequence of measured values.
            o   Scatter plots (XY plot)
                Scatter plots show the dependency of two variables. Additional information can be
                gained if the values are grouped.
                Potentially, several scatter plots can be combined to scatter plot matrices to show
                the interdependency of more than 2 variables.
                These plots are used to identify simple control strategies, typically in dependency
                of the outdoor air temperature.
                These plots are also called signature.
            o   Carpet plots
                Carpet plots are used to display long time series of a single variable in form of a
                colour map which often reveals pattern (like weekly operation patterns).
                These plots are used ti identify operation and occupancy schedules


For the analysis of the data, mainly scatter- and carpet plots will be used as they deliver
“characteristic patterns” e.g. for the energy consumption and the system temperatures. Time series
will be used as reference chart, in order to check the detailed time sequence of an unusual
behaviour that was detected with one of the other charts.
Important tools in visualization are filtering and grouping of data:
            o   Filter
                “Filter” denotes the creation of a subset of data that satisfies a certain condition
                (e.g. subset of the measurements of energy consumption below a certain outdoor
                air temperature). Thus, the behaviour of variables under certain boundary
                conditions can be studied.
            o   Grouping
                Data can be grouped according to certain conditions (e.g. heating energy can be
                grouped for workdays and weekends). Different operating points can thus be
                compared.


Even though the minimal data set will be recorded on an hourly or sub hourly time base, an
aggregation to daily or monthly values is reasonable in some cases in order to eliminated dynamic
effects.
The minimum amount of data for an analysis on the basis of visualizations is about 2-3 months if
the data is recorded in a swing season. Otherwise 5-6 months would be appropriate.
The next figures show examples of the different plot types.




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Figure 4   Time series plot.
           The data is plotted as a line. It will only be used for analysis of detailed time sequence of data points.
           The time resolution is 1 hour.


                            24


                            20


                            16


                            12
                     Time




                            8



                            4
                                                                                                       Color scale
                                                                                                            On

                            0                                                                               Off
                                 S   S   M   T   W T   F   S   S   M   T   W   T   F   S   S   M   T

                                                           Day / Date

Figure 5   Ideal carpet plot example (e,g, a fan running Monday to Friday from 8.00 to 18.00.)
           Here the course of each day runs along the y-axis from the “bottom” (y=0:00) to the “top” (y=23:00) and
           the days are plotted next to each other accordingly on the x-axis. The measurement value itself is
           portrayed in different colours. For days having a similar course of measurement values, the colour pattern
           is respectively similar. Such patterns can be visually identified very quickly.
           This kind of plots helps to identify occupancy and operation schedules, the timr resolution is 1 hour




Figure 6   Carpet plot for real weather and consumption data.
           This plot shows a real example. Naturally, the patterns are more blured as in the ideal example.



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Figure 7    Scatter plot of energy and water consumption versus outdoor air temperature, grouped by workday and
            weekends.
            These plots help to identify weather dependent control strategies. The time resolution for these plots is 1
            day. Each dot represents a daily mean value.
            The plots are also called signature or – for energy – energy signature.



For every group or system in the minimal data set (consumption, water circuits, AHUs, indoor
climate). These three plot types are generated. Depending on the kind of plot, different filters and
grouping might be applied.
In the following chapters the plots for the minimal data set are shown and their interpretation is
explained. The time series plots, however, are not shown as they are seldom used only used for
analysis.




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                                                                               Building EQ – Tool Description




      2.2.2 Energy and water consumption
The minimal data set contains all data for the total energy and water consumption. They are
displayed together with the weather data.

    2.2.2.1 Scatterplot (Signatures)
The Scatterplot for the consumption data shows the daily means of the consumption in terms of
power (energy consumption) or flow rate (water consumption) versus the outdoor air temperature.
The data is grouped by workdays and weekends. In the Building EQ project these plots are also
called signature (from energy signature).




Figure 8   Example for scatterplot of consumption versus outdoor air temperature.
           This building is supplied with district heat, electricity and water.
           From the difference in electricity and water consumption between workdays and weekends one can
           conclude that the occupancy during weekends is low. Accordingly one would expect a set back on
           weekends for the heating which is in fact visible.

Table 4 gives an overview over the general setup of the scatterplots for consumption.




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Table 4       General setup of scatterplot for energy and water consumption
              OAT = Outdoor Air Temperature


          Plots        Hints for interpretation                                                 Typical appearance
          (bottom                                                                               (simplified)
          to top)

          District      o    Typical appearance:
          heating vs         The energy consumption increases with decreasing OAT.
          OAT                Above a so called “changepoint temperature” the energy
                             consumption can be either A) zero (if no heat is needed in
                             summer), B) constant (e.g. for climate independent domestic
                             hot water consumption) or C) can have a negative slope.
                             Typically, the last case shows up, if there are (potentially
                             unnecessary) distribution losses in summer.
                             If the heating power is limited at low OAT (D), this can be a
                             sign of too low capacity, especially if there is no other heat
                             supply..
                        o    Difference between Workdays and weekends:
                             most non-residential buildings have a significantly lower
                             occupancy on weekends. If a set back is implemented in the
                             heating, workdays (red dots) and weekends (blue dots) will
                             show up as two distinct clusters. The better the set back
                             works, the bigger the difference will be. If red and blue dots
                             are completely mixed, a missing or deficient set back control
                             might be the reason
                        o    Changepoint:
                             The changepoint in most cases is located between 12 and
                             18°C OAT. If it is significantly higher, unnecessary heating
                             might be the reason.
                             If the setback is large, workdays and weekends can have
                             different change points
                        o    Slope:
                             The Slope corresponds to the energetic quality off building
                             envelope and ventilation (losses). The smaller the slope the
                             better the quality is.
                        o    Design Power:
                             The design power can be estimated from the plot if a linear
                             extrapolation of the heat consumption is made for the design
                             OAT. Comparing this value with the capacity of the
                             equipment, often over dimensioning can be recognized.
                        o    Base load in summer:
                             In most cases, a base load in summer corresponds to a
                             climate independent domestic hot water consumption. For
                             non-residential buildings this base load is often well below
                             10% of the maximum heating power. If the base load is
                             significantly higher, it should be checked if there are other
                             thermal processes in the buildings or if the energetic quality
                             of the building envelope and HVAC system is extremely high.
                             Otherwise there might be a potential for savings.
                        o    Scatter:
                             If the plot shows a big scatter or variance of the dots,
                             respectively, the reason for this can be either very divers


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Plots         Hints for interpretation                                                   Typical appearance
(bottom                                                                                  (simplified)
to top)
                    utilization of the building, a deficient control or a height
                    energetic standard a thereby a high influence of internal
                    and solar gains.

District       o    Typical appearance:
cooling             The typical appearance of cold consumption is very similar to
vs                  the heating consumption except of that the cooling power
OAT                 naturally increases with increasing OAT.

                    All of the statements made for district heating are applicable
                    to district cold, too, if the different correlation to OAT is kept
                    in mind.

                    The major difference can be the order of magnitude of the
                    base load in winter. As cooling in winter is typically needed
                    for zones with high internal gains such as data centres or
                    conference areas, the base load can be much higher than the
                    climate dependent part (especially if the zone doesn’t have a
                    ventilation and therefore can’t be cooled by outdoor air or
                    free cooling resp.)




Fuel           o    Typical appearance:
vs                  The appearance of the signature for fuels depend very much
OAT                 on the utilization of the fuel. If it is exclusively used for
                    heating or cooling the above mentioned hints for district heat
                    or cold can be applied.
                    However, if the fuel is used e.g. for a combined heat and
                    power plant or for other processes, the signature can
                    significantly deviate from what was shown above. In this
                    cases the system has to be studied in detail

Electricity    o    Typical appearance:
vs                  If the electricity is in large part used for cooling (e.g. for
OAT                 compressions chillers) or for heating (e.g. for heat pumps),
                    the signature can be similar to the signatures shown for
                    district heat or cold.
                    However, in many buildings the electricity consumption
                    comprises a lot of different utilizations and processes such as
                    lighting, HVAC, computer and other machines.
                    Therefore, if the electricity is not or only in small parts used
                    for heating and cooling, many buildings show a constant
                    signature (A).
                    If electricity is partially used for cooling, the electricity
                    demand might increase with increasing OAT. If cooling is not
                    a major consumer the slope is small and typically no
                    changepoint can be identified (B).
                    If the slope is negative so that electricity demand increases


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                                                                                 Building EQ – Tool Description




Plots     Hints for interpretation                                                   Typical appearance
(bottom                                                                              (simplified)
to top)
                when OAT is decreasing (C), this can often be connected (if
                no heating is provided by electricity) to increasing energy
                consumption for lighting in winter, especially if the building
                doesn’t have a distinct daylight usage.
           o    Difference between Workdays and weekends:
                Non-residential typically show a distinct difference between
                power consumption on workdays and weekends as the
                occupancy or utilization on weekends in many cases is low.
                The factor between load on workdays compared to
                weekends is typically about 2. If the factor is much lower
                then the base load is probably comparable high. This might
                represent a saving potential.
           o    Changepoint:
                A changepoint will only show up, if there is a climate
                dependent part of the consumption and if the this part is
                significantly more than 10% of the base load.
           o    Slope:
                The slope is also connected to the climate dependent part of
                the consumption. The magnitude of the slope is directly
                connected to the magnitude of the climate dependent load.
                Heating load or lighting can cause a negative slope, cooling a
                positive slope.
                Note if the effect of e.g. lighting and cooling together can
                compensate each other. Thus the signature in this case will
                be constant.
           o    Typical load:
                Many “normal” office buildings (without e.g. huge data
                centres) show an average electricity load of about 5 W/m² of
                used area. This value can be estimated from the plot if the
                used floor area is available.
                If the actual consumption for a building without a large
                consumer like a data centre is significantly above this value,
                this might be a hint to inefficient design or operation of the
                HVAC.

Water      o    Typical appearance:
vs              The water consumption shows very similar signatures than
OAT             the electricity consumption for partially the same reasons.
                Most non-residential buildings do not have an climate
                dependent part of the water consumption (A):
                A positive slope (B) might stem cooling towers that increase
                the water consumption in summer.
                The reason for a negative slope (C) can be reduced
                occupancy during the holiday season (this can be especially
                true e.g. for university buildings or schools)
           o    Changepoint:
                Water consumption of non-residential buildings does not
                have a changepoint in most cases as the climate dependent
                part is often not significant (only in the case of large cooling
                towers).
           o    Slope:

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Plots     Hints for interpretation                                                  Typical appearance
(bottom                                                                             (simplified)
to top)
                The slope can be connected to the climate dependent part of
                the consumption but also to occupancy effects.
            o   Difference between Workdays and weekends:
                Non-residential buildings typically show a distinct difference
                between water consumption on workdays and weekends as
                the occupancy or utilization on weekends in many cases is
                low.
                In fact the water consumption is the most important
                measurement in order to detect occupancy (also on an hourly
                time resolution).

overall   The combination of plots can deliver this information:
plot        o   Possible set backs:
                If water and electricity consumption show a distinct
                difference between workdays and weekends, a set back of
                heating and cooling should be possible in most cases. If this
                can not be identified in the corresponding signature, there
                might be a saving potential
            o   Simultaneous heating and cooling:
                This can be identified from studying the signatures for district
                heat or fuel and compare it to the signatures of cooling and
                electricity.
            o   Relationships:
                A base load for heating in summer often corresponds to the
                base load of water.
                A high base load of cooling in winter often corresponds to a
                base load of electricity




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    2.2.2.2 Carpet plots
The Carpetplot for the consumption data shows the hourly data of the consumption in terms of
power (energy consumption) or flow rate (water consumption). Furthermore the outdoor air
temperature and the global irradiation are shown as reference.
The main purpose of carpet plots is to identify operation and occupancy schedules and their
interrelationship.




Figure 9   Example of carpetplot of consumption values for a non-residential building
           Electricity and water consumption show the typical daily and weekly operation patterns while heating is
           blurred in winter at low outdoor air temperatures (OAT). This is due to an OAT dependent control of the
           operation schedule of the heating.
           Furthermore a abnormal pattern with increased consumption can be identified for water in September.
           In December the Christmas holidays can be identified by the reduced consumption values.




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Table 5 gives an overview over the general setup of the carpetplots for consumption.

Table 5          General setup of Carpetplot for energy and water consumption
                 OAT = Outdoor Air Temperature
                 *Note that the yellow parts in the simplified plots for the typical appearance might have changing values for
                 real buildings as load changes during day or season


          Plots              Hints for interpretation                                             Typical appearance
          (bottom to                                                                              (simplified)*
          top)

          Weather data         o   This data is only shown as a reference
          (OAT and
          Solar
          radiation)

          District             o   Typical appearance:
          heating                  Depending on the type of schedule and control, different
                                   patterns can show up in the carpet plot
                                   If the operation schedule is just time dependent, very
                                   regular daily and weekly patterns are the result (A) that
                                   reveals the set back on weekends and during night.
                                   Sometimes, if there is a set back on weekends, an extra
                                   heating time is added on Monday morning in order to
                                   compensate the reduced temperature after the weekend
                                   (B).
                                   In other buildings (see Figure 9) the heating schedule is
                                   extended in dependency of the OAT (C).
                                   If the weekend schedule is similar to the workday
                                   schedule pattern D will be the result, if no schedule at all
                                   is implemented (E).
                               o   Seasonal changes:
                                   Typically, the pattern for district heat diminishes or
                                   disappears in summer if the base load (e.g. for domestic
                                   hot water) is low compared to the space heating load. In
                                   swing season it might happen that the pattern is only
                                   visible for the morning hours after a set back during
                                   night. If cooling is needed in the afternoon on these days,
                                   this might represent a saving potential.
                                   If a set back control for holidays is active the pattern
                                   should show breaks during these periods.




          District             o   Typical appearance:
          cooling                  analogous to district heat
                               o   seasonal changes:
                                   analogous to district heat, except of that pattern
                                   diminishes in winter

          Fuel                 o   Typical appearance:
                                   Can be analogous to district heat or cold depending on
                                   the utilization of fuel.
                                   In case of special utilization (e.g. combined heat and
                                   power, other thermal processes) the system has to be


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Plots          Hints for interpretation                                                Typical appearance
(bottom to                                                                             (simplified)*
top)
                         studied in detail in order to interpret the pattern.

Electricity      o       Typical appearance:
                         The most typical pattern for the electricity consumption of
                         non-residential buildings is a regular daily and weekly
                         pattern (A). While the order of magnitude might change,
                         the difference of electricity consumption during occupied
                         hours is significantly different from the unoccupied hours.
                         Therefore the carpet plot of the electricity consumption
                         gives valuable information about occupancy schedules.
                         Deviations from this pattern can be a sign for energy
                         saving potentials.
                 o       Seasonal changes:
                         The seasonal change for electricity consumption depends
                         on the climate dependent part of the load. In most cases
                         the pattern is very constant over the year. However, as
                         electricity is very much connected to occupancy, holidays
                         should be clearly identifiable.

Water            o       Typical appearance:
                         The water consumption is - even more than the electricity
                         consumption - an indicator for occupancy as most water
                         consumption is connected to people (toilets, canteens).
                         Therefore, non-residential buildings often show a regular
                         pattern for the water consumption.
                         Deviations from this pattern can be sign for saving
                         potentials. E.g. if the flush of a toilet is stuck so that
                         water is constantly consumed, this shows up as a vertical
                         line in the plot.
                 o       Seasonal changes:
                         Water consumption in most cases is only dependent on
                         occupancy. Therefore holidays typically show up clearly in
                         these plots either as a break in the pattern or a reduction
                         of intensity.

overall plot   The combination of plots can deliver this information:
                     o      Adjusted schedules
                            The comparison of the patterns can reveal if the
                            heating or cooling is operated unnecessarily. Often this
                            happens during the holiday season. Electricity and
                            water consumption show a break in the regular
                            pattern (e.g. Christmas holidays). If heating and
                            cooling do not show the same break, this represents a
                            saving potential.




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      2.2.3 Heating and cooling circuits
For heating and cooling circuits, the minimal data set contains the supply and return temperature
as well as the control signal of the pump or the valve if available.
Principally, in order to analyze the data, the hydraulic scheme of the heating or cooling circuit has
to be considered. There are several typical arrangements to adjust the supply side to the load side
like using mixing valves to control supply temperature or throttle valves to control mass flow. AT
the same time one have to be aware if the measurements e.g of supply temperature are done on
the primary or secondary side of the system. This report will focus on the most typical
configurations in the demonstration buildings of Building EQ.
For heating circuits this is a configuration with a mixing valve and a secondary pump. The
temperatures are measured also on the secondary side of the circuit.




Figure 10   Scheme of a typical water circuit for heating with the data points of the minimal data set in the notation of
            the unified point naming convention (see 2.1.2)

For cooling circuits most of the measurements are made on the primary side (except for the
cooling coils of AHUs, see 2.2.4). Accordingly this chapter will concentrate on these
measurements.




Figure 11   Scheme of a typical water circuit for cooling with the data points of the minimal data set in the notation of
            the unified point naming convention (see 2.1.2).
            The red circle marks the primary side which will be the focus of this chapter.




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    2.2.3.1 Scatterplots (Signatures)
The Scatterplot for the heating and cooling circuits shows the daily means of the system
temperatures (supply and return) and the temperature difference (supply -return) versus the
outdoor air temperature. If the control signal of the pump is available, the temperature difference is
shown twice: once if the pump is ON and once if the pump is OFF. All data The data is grouped by
workdays and weekends. Furthermore the data (except of temperature difference for pump OFF) is
filtered with the operation signal of the pump, i.e. the values of the temperatures are only evaluated
for operation: pump ON.
The next figure gives two examples.




Figure 12   Examples of scatterplot for heating circuits
            Left side: example of a heating circuit without major faults. Supply temperature (T_SUP) and temperature
            difference (dT) show negative linear relationship to OAT. A set back on weekends can be identified from
            the difference of workdays (red) and weekends. If the pump is OFF, dT is around zero.
            Right side: example of a heating circuit with several deficiencies.
            Supply temperature (T_SUP) and temperature difference (dT) show negative linear relationship to OAT.
            But the set back on weekends is deficient as workdays and weekends are mixed. Furthermore, there is a
            high probability that the mixing valve is leaking as dT increases linearly with decresing OAT when the
            pump is OFF.




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Table 6 gives an overview over the general setup of the scatterplots for heating and cooling
circuits.

Table 6       General setup of scatterplot for heating and cooling circuits
              OAT = Outdoor Air Temperature
              * Note: the simplified plots show typical appearance for heating circuits.
              The plots for cooling circuits look similar except that the correlation to outdoor air temperature is positive.
              Consequently the plots for cooling circuits will be “mirrored” horizontally


          Plots          Hints for interpretation                                                 Typical appearance
          (bottom to                                                                              (simplified)*
          top)

          Supply            o   Typical appearance:
          temp.                 The supply Temperature (T_SUP) of heating circuits shows
          vs                    a negative correlation to OAT (A).
          OAT                   In some cases, a maximum temperature is reached well
                                before the minimal outdoor air temperature (B). In this
                                case it should be checked, if there are problems with
                                comfort. If NO, the slope of T_SUP can be probably
                                decreased.
                                For cooling circuits the temperature on the primary side is
                                very often kept constant (C)
                            o   Difference between Workdays and weekends:
                                If the building is not occupied during weekends (check
                                with energy plots), a set back in T_SUP should be visible.
                                In this case workdays (red dots) and weekends (blue dots)
                                will show up as two distinct clusters. The better the set
                                back works, the bigger the difference will be. If red and
                                blue dots are completely mixed, a missing or deficient set
                                back control might be the reason.
                            o   Changepoint (heating circuits):
                                If the control signal of the pump is available, no
                                changepoint is visible. If it is not available a changepoint
                                for T_SUP might result because then the data points
                                during periods when the pump is OFF will be included in
                                the plot (see below).
                            o   Slope (heating circuits):
                                The slope is dependent on the kind of emission system
                                connected to the heating or cooling system.
                                For heating circuits design supply temperatures can range
                                from 60-80°C (e.g. radiators) to 30-50°C (e.g. floor
                                heating).
                                For cooling circuits design supply temperatures can range
                                from 6 (e.g. from fan coil units) to 18°C (e.g. surface
                                cooling).
                                Accordingly the slope can be checked, if the type of
                                emission system is known.
                            o   Scatter:
                                If the plot shows a big scatter or variance of the dots,
                                respectively, the reason for this can be a deficient
                                control or (potentially manual) changes in the control
                                parameters. In any case the reason for a high variation
                                should be analysed in detail.


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Plots           Hints for interpretation                                                Typical appearance
(bottom to                                                                              (simplified)*
top)

Return            o   Typical appearance:
Temp.                 For heating circuits the return temperature (T_RET) is
vs.                   analogous to T_SUP:
OAT                   For cooling circuits T_RET will show a negative slope in
                      relation to the OAT.
                  o   Scatter:
                      The reason for a big scatter or variance of the dots can
                      be a varying load and or a deficient control.

Temperature       o   Typical appearance:
difference if         If the pump of a heating or cooling circuit is off, the
pump is OFF           temperature difference will be constant if there’s no
vs.                   influence by the environment (A).
OAT                   If the mixing valve is leaking, this can be identified by a
                      strong negative slope of dT (B). In this case a primary
                      pump forces a reduced mass flow through the circuit
                      resulting in an increasing dT when OAT is decreasing.
                      For cooling circuits the temperature difference on the
                      primary side is constant when the pump is OFF.
                  o   Difference between Workdays and weekends:
                      Again, an efficient set back can be detected by the
                      difference between workdays and weekends.




Temperature       o   Typical appearance:
difference if         For heating circuits dT if the pump is ON should show a
pump is ON            distinct negative slope (A).
vs.                   Cooling circuits will also show a negative slope with
OAT                   smaller magnitude due to the system temperatures (B).
                  o   Slope / maximum dT:
                      The slope or the maximum of dT (at low OAT for heating
                      and high OAT for cooling circuits) can be used to assess
                      the performance of the circuit.
                      If e.g. the dT for a heating circuit that supplies radiators is
                      too low (e.g. 5K at -5 °C) than the mass flow rate is too
                      for the current load. That is, electricity for pumping can
                      be saved.
                      This can be observed in many systems, especially cooling
                      systems that often show a dT around 1K.

overall plot    The combination of plots can deliver this information:
                  o   Relation of sensors
                      In order to assess dT, T_SUP has to be observed. The
                      lower (heating) or higher (cooling) T_SUP is the lower dT
                      will be. The analysis of dT has to observe this.
                      Also the comparison between dT when the pump is ON
                      and OFF could be valuable information for the analysis.


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    2.2.3.2 Carpet plots
The Carpetplot for the heating and cooling circuits shows the hourly data of the system
temperatures and the control signals if available. Furthermore the outdoor air temperature and the
global irradiation are shown as reference.
The main purpose of carpet plots is to identify operation schedules.




Figure 13   Example of a carpetplot for a heating circuit
            It shows weather data (OAT and solar radiation) as reference, supply and return temperature (T_SUP,
            T_RET), the temperature difference (dT) and the control signal of the pump.
            It can be seen that the operation (CTRL-SIG) changes from weekly patterns in Sept / Oct to almost
            constant operation in Dec. This is due to a OAT dependent control for the schedule of the pump. At the
            same time a pre-heating mode in the early morning hours can be observed as T_SUP and dT shows
            higher values then.




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Table 7 gives an overview over the general setup of the carpetplot for heating and cooling circuits.

Table 7       General setup of Carpetplot for heating and cooling circuits
              OAT = Outdoor Air Temperature
              *Note that the yellow parts in the simplified plots for the typical appearance might have changing values for
              real buildings as load changes during day or season


          Plots           Hints for interpretation                                            Typical appearance
          (bottom to                                                                          (simplified)*
          top)

          Weather data      o   This data is only shown as a reference
          (OAT and
          Solar
          radiation)

          Supply            o   Typical appearance:
          Temperature           For T_SUP the same patterns are visible than for district
          (T_SUP)               heat, cold o fuels (see 2.2.2.2)
                                typical patterns are: only time dependent (A), longer pre-
                                heating/cooling after set back on weekends (B), extension
                                of operation in dependence of the OAT (C), Without set
                                back on weekend (D) or constant operation (E).
                                In case of constant or extensive operation, the schedules
                                should be checked in order to avoid unnecessary
                                operation.
                                Furthermore in some cases it can be observed that T_SUP
                                during set back periods is higher (heating) or lower
                                (cooling) than in normal operation. Leakage of a valve can
                                be the reason for this.
                            o   Seasonal changes:
                                Typically, the pattern is more prominent in the “active”
                                season, i.e. in winter for heating and summer for cooling.




          Return            o   Typical appearance:
          Temperature           analogous to T_SUP
          (T_RET)           o   seasonal changes:
                                analogous to T_SUP

          Temperature       o   Typical appearance:
          Difference            dT also shows the same patterns than T_SUP, but often
          (dT)                  more clearly. If the pump in the corresponding circuit is
                                operated at constant flow, dT has a linear relationship the
                                the power that is provided by the circuit.
                                However, if the pump is variable flow than the
                                interpretation is less easy but still it can carry valuable

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Plots          Hints for interpretation                                              Typical appearance
(bottom to                                                                           (simplified)*
top)
                     information.
                 o   seasonal changes:
                     analogous to T_SUP

Control          o   Typical appearance:
Signal of            Principally, the CTRLSIG can have the same patterns than
pump or              T_SUP.
Valve                The control signal, if available, gives direct feedback
(CTRLSIG)            about the operation schedule of the circuit.
                     If it is not available, the other plots sowed that some if
                     the “scheduling information” is also carried by them

overall plot   The combination of plots can deliver this information:
                 o   Adjusted schedules
                     The carpet plot can be utilized to check if the operation
                     schedule of pumps and valves are adjusted.
                     Also a comparison to the energy signatures (2.2.2.2)
                     might help in detecting unnecessary operation.




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      2.2.4 Air handling units (AHUs)
For AHUs, the minimal data set comprises the air side temperatures and the water side
temperatures of all coils as well as the control signal of the fans and pumps if available.
Principally, in order to analyze the data, the configuration of the AHU has to be considered. In the
Annex, a typology of AHUs is shown. Accordingly, a fully equipped AHU can have up to three coils
(heating, cooling, pre-heating/cooling) and a heat recovery system. Note that the heat recovery can
also be another coil unit.
As the coils themselves are treated in the same way as the heating and cooling circuits, only the
plots for the air side of the AHU are shown here.




Figure 14   Simplified scheme of a fully equipped AHU (only humidification is missing)




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    2.2.4.1 Scatterplots (Signatures)
The Scatterplot for the AHUs show the daily means of the system temperatures on the air side of
the AHU (supply air, exhaust air, difference supply-exhaust) and the temperature differences
(supply -return) of waterside of the coils versus the outdoor air temperature. The air side
temperatures are filtered with the control signal of the fans, so that only temperatures during
operation are shown. The same applies to the water side temperatures concerning the
corresponding pumps. All data is grouped by workdays and weekends. The next figure gives an
example.




Figure 15   Examples of scatterplot for air handling unit
            This AHU has a heating and a cooling coil. The difference of supply and exhaust air temperature (dT supa-
            exha) reveals if the zone is heated or cooled by the supply air. If the difference is above 0 the zone is
            heated and vice versa. The supply and exhaust air temperature show a corresponding behaviour. The
            temperature difference on the water side of the coils (CC dTsup-ret and HC dTsup-ret) shows if the coils
            are used to condition the supply air. While the heating coil is working below 15°C OAT, the cooling coil is
            not needed up to 22°C.


                                                                                                            Page 29
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Table 8 gives an overview over the general setup of the scatterplots for AHUs.

Table 8       General setup of scatterplot for heating and cooling circuits
              OAT = Outdoor Air Temperature


          Plots          Hints for interpretation                                               Typical appearance
          (bottom to                                                                            (simplified)*
          top)

          Supply air       o    Typical appearance:
          temp.                 The appearance of the supply air temperature (T_SUPA) is
          vs                    dependent on the configuration of the AHU.
          OAT                   If the supply air is neither heated nor cooled T_SUPA will
                                follow the OAT (A). In dependency of the presence of a
                                heat recovery the slope can be near one or well below 1 if
                                the efficiency of the heat recovery is high.
                                If the air is heated during winter but not cooled in summer
                                T_SUP will show patter (B).
                                If the air is kept on a constant temperature by heating in
                                winter and cooling in summer pattern (C) applies.
                                If there is a dead band between heating and cooling in
                                which the building or zone is operated in free float,
                                pattern (D) is probable
                           o    Difference between Workdays and weekends:
                                If the fans are not operated during unoccupied hours
                                including the weekend, the data for weekends won’t
                                show up in the plot as the temperatures are filtered with
                                the control signal of the fan.
                                If the fan is operated on weekends the pattern is
                                dependent on the kind operation and can be any pattern
                                discussed above. However, it should be checked if the
                                AHU really has to be operated n weekends.
                           o    Changepoint
                                The exact position of a changepoint in pattern (B) and (D)
                                is dependent on the energetic quality of the building and
                                on the internal gains. The more internal gains and the
                                better the energetic quality, the lower the OAT at the
                                position of the changepoint(s) will be.

          Exhaust air      o    Typical appearance:
          temp.                 The Exahust air will either show pattern (B), (C) or (D) (see
          vs                    T_SUPA) in dependency of the operation mode.
          OAT




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                                                                                  Building EQ – Tool Description




Plots          Hints for interpretation                                                Typical appearance
(bottom to                                                                             (simplified)*
top)

Temperature     o    Typical appearance:
difference           If supply air is not treated, the pattern of the temperature
Supply –             difference between supply and OAT (dTsupa-outa) will
OAT                  resemble pattern (A).
vs                   If the supply air is only heated, pattern (B) applies.
OAT                  If the supply air is heated and cooled pattern (C).
                     (Note: if the supplied zone has several emitters for heat
                     and cold the pattern may differ, depending on the control
                     and capacity of the other emitters)
                     Principally, If dT is positive the supply air is heated (either
                     by fan, heat recovery or heating coil) and if negative the
                     supply air is cooled (heat recovery, cooling coil adiabatic
                     cooling)
                o    Difference between Workdays and weekends:
                     Similar to T_SUPA, the pattern during hours without or
                     with low occupancy can be quite different depending on
                     the operation mode.

Temperature     o    Typical appearance:
difference           If supply air is not treated, the pattern of the temperature
Supply –             difference between supply and exhaust air (dTsupa-exha)
exhaust air          will resemble pattern (A).
vs                   If the supply air is only heated, pattern (B) applies.
OAT                  If the supply air is heated and cooled there will be a
                     negative slope at high OAT like in (C).
                     (Note: if the supplied zone has several emitters for heat
                     and cold the pattern may differ, depending on the control
                     and capacity of the other emitters)
                     Principally, If dT is positive the zone is heated by the supply
                     air and if negative the zone is cooled by the supply air.
                o    Difference between Workdays and weekends:
                     Similar to T_SUPA, the pattern during hours without or
                     with low occupancy can be quite different depending on
                     the operation mode.

temperature     o    Typical appearance:
difference           The temperature difference on the water side of the
supply-              cooling coil will show a negative slope at high OAT.
return          o    Changepoint/Slope
(water side)         The slope and position of the changepoint are dependent
of cooling           on the energetic quality of the building and on the internal
coil                 gains. Typically, the changepoint is between 15 and 20°C
                     OAT.
                     If the magnitude of the temperature difference at high
                     OAT is small (<=1K), this could be a sign that the mass
                     flow through the coil is too high.

temperature     o    Typical appearance:
difference           The temperature difference on the water side of the
supply-              heating coil will show a negative slope at low OAT.
return          o    Changepoint/Slope
(water side)         Typically, the changepoint is between 10 and 15°C OAT.

                                                                                                        Page 31
                                                                                Building EQ – Tool Description




Plots          Hints for interpretation                                              Typical appearance
(bottom to                                                                           (simplified)*
top)
of heating           If the magnitude of the temperature difference at low OAT
coil                 is small (<=5 K), this could be a sign that the mass flow
                     through the coil is too high.

overall plot   The combination of plots can deliver this information:
                 Correlations:
                 As the interdependency of the different variables in a physical
                 sense is quite strong, the correlations given by the plot can
                 help to analyse the operation (e.g. if the operation of a cooling
                 coil fits with the behaviour of the supply air temperature)




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                                                                                    Building EQ – Tool Description




    2.2.4.2 Carpet plots
Besides the system temperatures on the air and water side, the carpetplot of the AHUs shows the
control signal of the fans.




Figure 16   Example of a carpetplot for an AHU
            It shows weather data (OAT and solar radiation) as reference, supply air temperature (T_SUPA), the
            difference of supply and outdoor air (dTsupa-out), the difference of supply and exhaust air (dTsupa-exha),
            the control signal of the fans and the water side temperature differences of the heating and cooling coil.
            From the control signal of the fan the very regular operation can be seen. The air side temperatures show
            the transition from cooling mode in summer to heating mode in winter. The temperature difference on the
            water side of te cooling coil reveals that cooling is only needed for comparable short times while the
            heating coil is operated from October on.


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Table 7 gives an overview over the general setup of the carpetplot for heating and cooling circuits.

Table 9       General setup of carpetplot for Air handling Units
              OAT = Outdoor Air Temperature
              *Note that the yellow parts in the simplified plots for the typical appearance might have changing values for
              real buildings as load changes during day or season


          Plots           Hints for interpretation                                                 Typical appearance
          (bottom to                                                                               (simplified)*
          top)

          Weather data      o   This data is only shown as a reference
          (OAT and
          Solar
          radiation)

          Supply Air        o   Typical appearance:
          Temperature           The pattern for T_SUPA is dependent on the operation
          (T_SUPA)              schedule and on the type of control. Typically, if the AHU
                                is operated during normal working hours, pattern (A)
                                shows up. If the weekends are included, pattern (B)
                                applies and constant operation might show up as pattern
                                (C).
                                However, the appearance is also highly dependent from
                                the position of the AHU and the temperature sensors as
                                these determine which temperatures will be measured
                                during periods when we AHU is OFF. If e.g. the AHU and
                                the sensor is installed outside the building on the roof,
                                the temperature during non-operation hours will follow
                                very much the OAT. On the opposite, if the AHU is placed
                                inside the thermal envelope, the supply air temperature
                                when the AHU is OFF will be near the room temperature.
                                This can have crucial influence on the visibility of the
                                operation patterns and has to be kept in mind.
                            o   Seasonal changes:
                                Again seasonal changes are dependent on the type of
                                control. They will be strong if the supply air is either not
                                treated at all or if the supply air is excessively used for
                                heating and cooling. If T_SUPA is kept constant
                                throughout the year, no changes will be visible, at least
                                during operation.

          temperature       o   Typical appearance:
          difference            The patterns are analogous to T_SUPA.
          supply air –          If the difference shows positive values, the supply air is
          OAT                   heated by the AHU (this could be either by the fan, a heat
          (dTsupa-out)          recovery, or a heating coil.). If the difference is negative
                                supply air is cooled (either by cold recovery, cooling coil or
                                adiabatic cooling)
                            o   seasonal changes:
                                analogous to T_SUPA

          temperature       o   Typical appearance:
          difference            The patterns are analogous to T_SUPA.
          supply air –          If the difference shows positive values, the supply air is
          exhaust air           delivering heat, if the difference is negative the supply air


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Plots            Hints for interpretation                                               Typical appearance
(bottom to                                                                              (simplified)*
top)
(dTsupa-               is delivering cold to the zone.
exha)              o   seasonal changes:
                       analogous to T_SUPA

Control            o   Typical appearance:
signal of fans         The patterns are analogous to T_SUPA.
                       If the fan is operated constantly, the necessity of that
                       operation should be checked (in many cases this is
                       unnecessary). The carpet plot for consumption (see
                       2.2.2.2) could help in this task, as electricity and water
                       consumption gives valuable feedback about the
                       occupancy schedule.

Temperature        o   Typical appearance:
difference on          The patterns are analogous to T_SUPA but typically
water side of          should show up only in the cooling season.
cooling coil           If the pattern is visible in winter too, the control should be
(CC dTsup-             checked.
ret)               o   seasonal changes:
                       pattern should typically only show up in summer

Temperature        o   Typical appearance:
difference on          The patterns are analogous to T_SUPA but typically
water side of          should show up only in the heating season.
heating coil           If the pattern is visible in summer too, the control should
(HC dTsup-             be checked.
ret)               o   seasonal changes:
                       pattern should typically only show up in winter.

Overall plot     The combination of plots can deliver this information:
                   o   Adjusted schedules:
                       The carpet plot contains rich information about the
                       operation schedules of the different components of the
                       AHU. It can be easily checked if they are adjusted.
                   o   Simultaneous heating and cooling
                       This can be detected by comparing dT for the cooling and
                       heating circuit. If both are operated at the same time, the
                       control of the AHU should be checked.
                   o   Capacity of cooling coil not sufficient.
                       If the dT of the cooling coil indicates operation but the
                       temperature difference of supply air and exhaust air is
                       positive, the control and capacity of the cooling coil
                       should be checked.
                       The same applies for the heating coil if ventilation is
                       providing a significant part of the heating power to the
                       zone.




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   2.3 Building specific benchmarks

     2.3.1 General model structure
In the course of the Building EQ project it became apparent that one major drawback concerning
the EPBD is the diversity of the different national implementations in the Member States. That is,
there is no common data set for all Member States that can be exploited for performance analysis.
Consequently, there is no “natural” common basis for the European tool. The consortium therefore
decided to make use of simplified models based on CEN-standards and use “condendsed”
parameters. In this way the model should be valuable for many Member States.
The aim is to provide a model structure for the building zones and the HVAC equipment that uses
(very) simple component models but which in turn is able to describe (very) complex system
schemes.
This model will be used to compare the real measured energy consumption to the model prediction
in order to find faults and optimization potentials.
Only 3 principally different component models are necessary for this:
           o   Building Zone
               A model for the building zone is needed to calculate the heating and cooling load of
               the building. The model was adopted from the CEN 13790.
           o   Air handling Unit (AHU)
               Air handling units are used for heating, cooling, humidification and dehumidification
               of supply air. Furthermore they can include a heat recovery and more or less
               sophisticated controls for the air volume. For several configurations of AHUs,
               models were implemented that comprises simple models for the sub-components
               such as heating and cooling coils, fans, etc.
           o   System Component
               All other system components for emission, distribution, storage and generation
               were modelled by rather simple “efficiency” models which are called “efficiency
               boxes” in this report.




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Figure 17 gives a simplified scheme of the overall system model, showing the three different types
of component models. All “boxes” in the systems part will be efficiency boxes.


                             Energy flow in the system

     Building                                 System
    Zones                                     Efficiency Boxes

            Zone 1                               Trans-          Distri-              Gene-
                                 heating                                   Storage                     Fuels
                                                 mission         bution               ration

            Zone 2                                                                                  District heat
                                                 Trans-          Distri-              Gene-
                                 cooling                                   Storage
    Air Handling Units                           mission         bution               ration
                                                                                                    District cold
            AHU 1
                                                 Trans-          Distri-              Gene-
                                  electr.                                  Storage                   electricity
                                                 mission         bution               ration
            AHU 2




                            EBPD – CEN Calculation Path




Figure 17       Simplified model structure (without interconnections in the “System”-Block)




        2.3.2 Zone model
In order to be able to compute the thermal behaviour of the building a multi-zone-model has to be
applied with several thermal zones according to different utilization and conditions in the building.
The consortium decided to implement the so called simple hourly method which is described in the
EN ISO 13790:2008 which is one of three methods for calculating the load of the building (the
other two are: monthly method and dynamic simulation, see Figure 18).
The simple hourly method has several advantages compared to the others. The most important for
Building EQ are:
    o    It can be easily implemented as it does not require a numerical solver. The calculation is
         strait forward.
    o    Hourly schedule, e.g. for set backs and the distribution of load among several emitters or
         generators can be directly input or calculated without dealing with reduction factors or
         assumptions.
However, the comparison with the measured consumption data will be done on a daily or weekly
time resolution. Climatic data for the calculation will be available from the demonstration buildings
as it is part of the minimal data set.




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In order to limit the number of parameters of the model, no detailed calculation of thermal
characteristics of wall and windows is done inside the tool. General data like overall U-values for
walls and windows and optic characteristics for windows are to be necessary instead. As these
factors might be calculated differently in different Member States this serves also the
exchangeability. Furthermore data for internal gains and for schedules has to be given.




Figure 18   Flow chart of calculation procedure described in EN ISO 13790 and links with other standards)



The zone model is based on an equivalent 5 Resistance 1 Capacitance (5R1C) model which is
shown on Figure 19. All building and systems input data can be modified each hour using
schedules.




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Figure 19   5R1C scheme of zone model for the hourly method



The model allows calculating the heating and cooling load that is necessary to maintain a set-point
temperature. The heat transfer by ventilation is directly connected to the air node temperature. The
heat transfer by transmission is split in two main parts: the transmission through window and the
transmission through opaque wall. The flux due to internal and solar sources is divided aming the
three nodes (air node, central node, mass node).




Figure 20   Building zone temperature behavior versus system behavior

Figure 20 shows possible operation modes using the hourly method considering a maximum power
for heating and/or cooling emitters. Mode 1 and 5 are considered when the maximum power of the
heating or cooling emitters is not sufficient to meet the load (and thus to reach the set-point
temperature). Mode 2 and 4 are considered when the building zone is at part load and only a part
of the maximum power is released to the air node. Mode 3 considers the free-floating operation.



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        2.3.3 Model for Air Handlin Units (AHUs)
Air handling units can have a lot of different configurations (see Overview in the Annex). Therefore
only the most typical configurations were considered in the framework of Building EQ. However, at
the time of the editing of this report only the most simplest systems are implemented. These are:
    o    Exhaust air system
    o    AHU, fans only (with or without heat recovery)
    o    AHU, fans and heating coil (with or without heat recovery)
    o    AHU, fans and cooling coil (with or without heat recovery)


                 SUP_Air                                               EXH_Air




                 EXH_Air                                               OUT_Air




                 SUP_Air                 +
                                                                       EXH_Air




                 EXH_Air                                               OUT_Air




                 SUP_Air             -   +
                                                                       EXH_Air




                 EXH_Air                                               OUT_Air


Figure 21    Simplified schemes of the air handling units that can be handled by the tool



The different configurations are modeled as combinations of the corresponding sub-components.
The models for the sub components (fan, coils, and heat recovery) were implemented following
either the ASHRAE secondary HVAC toolkit 3 or the results of the SAVE project AUDITAC 4.
For the configurations with heating and/or cooling coil the hygienic air flow rate as well as a
maximum air flow rate for heating and cooling can be specified. Furthermore minimal and maximal
supply air temperatures for heating and cooling can be defined.
If heating load occurs, first the supply air temperature is increased up to the maximum value. If the
power is not sufficient, the air flow rate will be increased up to the maximal air flow rate. The
additional air flow is provided purely as recirculation air via the recirculation air damper.


3
  Brandemuehl, M.J., S. Gabel, and I. Andersen. 1993. A toolkit for secondary HVAC system energy
calculations (629-RP). Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers,
Inc.
4
 AUDITAC "Field Benchmarking and Market Development for Audit Methods in Air Conditioning", IEE
project, see: http://www.energyagency.at/(en)/projekte/auditac.htm

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The cooling is controlled in the same way except when outdoor temperature is below the minimal
supply air temperature for cooling. In this case the cooling power will be provided by increasing the
outdoor air flow rate.
The models take into account power limitations given by the maximum air flow rates and supply air
temperatures specified as well as the limitations of the generator which supplies the AHU.

      2.3.4 General model for system components
According to prEN 15316 each component of the system is modeled as an efficiency box which
models the efficiency of a component in dependence of the operation mode. Generally an
efficiency box could be an emitter, a distribution circuit, a hot/cold storage or a generator. Among
the generators there are boilers, chiller, CHP units, etc. The same approach is made for heating,
cooling, domestic hot water and ventilation systems (see chapter 2.3.5).
Each box can in principle be implemented with different level of complexity. The simplest one is to
assign a constant efficiency (e.g. efficiency of the distribution circuit), whereas the sub-model for a
boiler or a chiller could be more complex.
Figure 22 illustrates the principle input and output data as well as the calculation for a given sub-
system (box). Each sub-system considers an input of energy (electrical or thermal energy) and
auxiliary electricity (W). The auxiliary electricity is converted to thermal energy and partially added
to the system output.
The outputs are the energy (electrical or thermal) that is delivered to the next connected
component and the thermal losses.


            CEN Approach
                                                                                              Information flow

                                                                          W provided
                              Q required to                                                Q required from
                                  the component               System                          the component
                                                             Component
                                                             Functional
                                                                BOX
                                                                                                  Energy flow

     Box Characteristic Equation
                                                                                              W electricity
  Qreq from = Qreq to + (QLost − kComp ⋅ W prov )
                      −                                                  Q lost
                                                                                              Q thermal energy
                                                                                              (heating and cooling)

                      ∑ (η          ⋅ WAUX )i
                       N
                                       &                    ⎛              ⎞
                                                                 1
                                                    QLost = ⎜           − 1⎟ ⋅ Qrequired to the component
                              AUX
            kComp =   i =1
                                                            ⎜η             ⎟
                              &
                             W provided                     ⎝ component    ⎠


Figure 22     simplified box model used in Building EQ

In addition to just propagating energy flows through the system, the Building EQ team developed
an approach that takes capacity limits into account. That is, if the capacity of one of the
components is not sufficient, the set point for heating and cooling of the zones will not be met. This
is a major improvement compared to pure design tools as deficient system configurations with
undersizing can be taken into account.


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      2.3.5 Overall system model
The overall system model of a non residential building that consider all the energy consumption
(H= heating, W= domestic hot water, V= ventilation, C= cooling, RH= re-heating, AHU=air handling
unit, etc.) could be very complex and contain a lot of sub-system boxes. However, one
simplification is that no feedback loops are allowed.
The figure below shows how a system could be represented. Building box and air handling units
are not considered in this scheme. The system comprises the heat production for DHW, ventilation,
re-heating and heating with one generator and the cold production for ventilation and building
cooling with a chiller.




Figure 23   Example of a complex system, according to CEN approach




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    2.4 Automated outlier detection
The aim of the automated outlier detection is to automatically detect unusual energy or water
consumption on a daily basis. The basic idea is to identify the so called signatures of the
consumption with a multiple linear regression model that is combined with a day typing process.
“Signature” denotes the relationship of the consumption with other variables like outdoor air
temperature, irradiation or indoor air temperature. Typically, graphical representations of
signatures are just showing the relation to outdoor air temperature.




Figure 24   Examples for Energy signatures on a daily basis.
            Concerning the change point, you can distinguish between a temperature dipendendo change ooint (left
            chart), a time dependent changepoint (middle) and no changepoint (right)



The examples in Figure 24 show that the identification of signatures should involve:
    o   To distinguish between different day types
        A day type in this context means a certain consumption pattern. Typically for non-
        residential buildings at least Workdays and weekends have to be distinguished.
    o   To identify a changepoint
        A changepoint denotes a value of one of the parameters in the regression model at which
        the model structure changes. A typical example is the signature for heating energy that
        significantly changes over outdoor air temperature, as at high temperatures no heating is
        needed any more.
        Also a time dependent change point is possible when heating or cooling is turned off and
        on just according to the calendar.




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The algorithm consists of a training phase and an application phase.
    o   Training phase:
        In the training phase, the parameters of the regression model are identified by using a
        training data set. The training data set should ideally comprise at least 5 months of
        measured data. First the measured data itself is tested for measurement errors then a
        clustering process is performed to find day types (days with similar consumption pattern).
        The last step is the identification of parameters of the regression model.
    o   Application phase:
        In the application phase new data (new days) are tested for outliers. The first step is
        assignment of a day type, followed by the prediction of the regression model and the
        comparison between predicted and measured value. If the measured value s significantly
        off the predicted it is marked as an outlier.




Figure 25   simplified scheme of the outlier detection algorithm




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    3 User Guide
The features and functionality described in the previous chapters are provided to the end-user by a
graphical user interface (GUI). Principally, creating a new project includes the following steps:
    o   Importing measured data:
        As all functionality is based or connected to measured data, importing this data is the first
        step.
    o   Importing or Editing the meta data:
        Meta data is needed to let the programme know, which data points are available. The meta
        data can be either imported from a structured ASCII file or manually edited in the
        programme
    o   Performing an analysis:
        After measured data and meta data are available, an analysis can be performed.
The general structure of the main window of the Building EQ tool looks like this:

     Symbol bar for quick access
     of commands and tool windows    Tabs for selection of different
                                     views of data and results




                  Tree View window for             Main Window for display   Time navigation to
                  navigation and selection         of data, meta data and    select periods of time
                  of options                       results



Figure 26    General Structure of main window of the tool




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The symbol bar contains symbols with the following underlying functionality:




                                                                                 Close program

                                                                             Switch to plot view
                                                                             (displays plot if a sensor
                                                                             is selected in the tree view)

                                                                     Switch to model view
                                                                     (to define or change a model)

                                                                  Switch to sensor view
                                                                  (examine a single sensor that is
                                                                  selected in the tree view)

                                                           Switch to sensor group view
                                                           (examine sensor group that is
                                                           selected in the tree view)

                                                Switch to canvas view

                                        Show/hide log window
                                        (the log window displays potential
                                        error messages and warnings)

                                  Show/hide tree view

                            Open existing project
                    Start new project

Figure 27   General Structure of main window of the tool



The following chapters describe how to work with the GUI.




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    3.1 Creating a new project

        3.1.1 Importing raw data
The first step of every project is to import the measured data for the analysis. In the “Data” menu,
choose “New project”. A dialog window appears where you will have to enter:
    o    Project path:
         This is the folder where your project will b saved in your directory
    o    Project name
         Name under which your project will be saved
    o    Check box “import data”:
         If this check bow is turned off, just the basic file structure will be created. Data can also be
         imported later on. For this you have to choose “import data” from the “Data” menu.




Figure 28    Dialog box for data import, specification of project path and name



After clicking “Next”, another dialog window will open, were you have to enter:
    o    “from”:
         This is the directory path of the folder that contains the raw data.
         The raw data has to be provided in one or several files that have the format that was
         specified in 2.1.3.
    o    “wildcard”
         A name containing a wildcard that is used as filter for the file import.
         E.g. “*.csv” will import all csv files, while “building1*.csv” will only import those files thw
         name of which starts with “building1”.
    o    “Sensor group name”
         Specify a name for the sensor group, e.g. “raw_data”. You will find the imported data under
         this name in the tree view after the import.
    o    “time format”
         specify a time format. Default is %d.%m.%Y %H:%M which you should not change!
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    o   “error value”
        Specify a value that marks missing or erroneous data in the file. Default is -999
    o   “time column”
        Specify which column is the last column of the timestamp (containing the clock time). E.g. if
        date and time are together in column 1, value will be 1. If date and time are in two columns,
        value will be 2.
    o   “date column”
        Only needed if the date of the timestamp Is specified in the raw data in a column after the
        time. E.g. if in column 1 the time is specified and in column 2 the date, the value will be 2.
        Default is “None”.
    o   “Delimiter”:
        The default is “;”. If your raw data has another separator that divides the single columns of
        data, you can specify it here.
    o   Check box “import meta data”
        If you want to continue with the import of the meta data right after the import of the raw
        data, keep the box check. If not uncheck the box. The import of meta data can also be
        invoked manually via the “import meta data”-option in the “Data” menu (see 3.3.1).
        Note, if you do this, you will be also asked to process the data (see 3.4)




Figure 29   Dialog box for import raw data, specification of raw data files



If you uncheck the “import meta data” box, your input will be displayed again and you will be asked
to start the import by clicking on the “Finish” button.
Please note that the import of data can take up to some minutes depending on the time resolution
and the amount of data you have available. E.g. one year of data with 5 minutes time resolution
and 300 data points will sum up to about 240 MB of data. This will take about 4-6 minutes for the
import, depending on your system.




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Once the raw data is imported, the tree window will show a navigation tree which shows you the
project on the highest level. Below the project there will appear the sensorgroup that contains the
raw data. If you click on the “+” symbol of this sensorgroup you will have access to the single
sensors.




Figure 30   Tree view showing the project the “raw data” sensor group and the single sensors.


    3.2 Inspecting the data
If the raw data is imported and you have reached a screen that looks like Figure 30, you can start
to inspect the data.

      3.2.1 Sensorgroup options
If you double click on the raw data sensor group symbol, the data will be displayed as table view in
the main window (Figure 31). You will see the time stamps in the first column, the original data
point names of the raw data in the first row. With the time navigation you will have the change to
“move” through the data. The data can just




Figure 31   Main window with table view of the raw data sensor group


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                                                                       Building EQ – Tool Description




The main window has two other tabs that give access to the following views:
   o   “meta data”
       The meta data of all sensors in the raw data is displayed and can be edited. For further
       explanations see 3.3.
   o   “statistics”:
       A simple static of the raw data is displayed. For each sensor the min, max, mean, standard
       deviation and percentage of invalid values are shown.
       Note: the statistics apply to ALL of the available data, not only the period that was chosen
       with the time navigation.




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                                                                         Building EQ – Tool Description




        3.2.2 Sensor options
If you double click a single sensor, the data will be displayed as table view like for the sensorgroup
(Figure 31) but just for the single sensor.
Furthermore, the main window for sensors has four other tabs that give access to the following
views:
    o    “meta data”
         The meta data of the sensor is displayed and can be edited. For further explanations see
         3.3.
    o    “statistics”:
         A simple static of the sensor is displayed (same as fro sensor group, see 3.2.1).
    o    “plot”:
         A time series plot of the sensor is displayed.
    o    “data quality”:
         The quality of the sensor is displayed as carpet plot. A value of zero denotes data without
         errors, i.e. 100% valid data.




Figure 32    Main window with plot view of a single sensor




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    3.3 Importing / editing meta data
In order to use any of the analysis routines the tool has to be “told” which kind of data is available.
This is done by providing the meta data as described in 2.1.4.
This can be done in two ways: either by importing the meta data from an ACSII file or by editing it
manually. Principally, if the number of sensors is significant (e.g. >30) the creation and import of an
ASCII meta data file will make sense.

        3.3.1 Importing meta data
In order to import meta data, it has to be provided in an ASCII (csv) file the structure of which was
described in 2.1.4. This file has to be prepared outside the tool, e.g. with a spreadsheet tool that
has ASCII export function (e.g. Microsoft Excel) or a simple ASCII editor.
To start the import choose “Import meta data” in the data menu. A dialog window will open and you
have to enter:
    o    “from”:
         enter the path and file that contains the meta data.
    o    “Delimiter”:
         The default is “;”. If your meta data file has another separator that divides the single
         columns, you can specify it here.
    o    Check box “process data”
         If you want to continue with processing the data right after the import of the meta data, keep
         the box checked. If not, uncheck the box. The processing of the data can also be invoked
         manually via the “Process data”-option in the “Data” menu (see 3.4).




Figure 33    Dialog window for import of meta data



If you click next you will be asked to confirm your input by clicking on the “Finish” button. After the
import of meta data is finished, you can view and edit it under the meta data view of the
sensorgroup and sensors.
To do so, you have to double click a sensorgroup or a sensor and choose the meta data tab.
Figure 34 shows an example of te raw data sensorgroup.

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                                                                                    Building EQ – Tool Description




Figure 34   Main window with a table view of the meta data of a sensor group




      3.3.2 Edit the meta data
In order to edit the meta data, you can double click on a meta datum for a sensor. You can enter
any text, however, you have to follow the unified point naming convention explained in 2.1.2.
If you already imported meta data from an ASCII file, all possible entries in the single fields will be
available via an drop down list from which you can choose.




Figure 35   Main window with a table view of the meta data of a sensor group, The drop down list for category
            “system” is activated for one sensor.




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    3.4 Process data
After raw data and meta data is imported or edited, respectively. You can process the data. This is
necessary in order to use the analysis routines. The processing will perform the following tasks:
    o   Sample the raw data to provide hourly, daily and weekly data.
    o   Do the pre-defined visualizations according to 2.2


You can invoke the data processing by choosing the “Process data”-option in the “Data” menu.
After processing the tree view is extended with new so called filtergroups. Filtergroups contain
processed data, while sensorgroups contain raw data.




Figure 36   Tree view after the processing of the data. Besides the raw data, the filtergroups for hourly, daily and
            weekly data are displayed as well as the visualizations.



Please note that the processing of the data can take up to several minutes depending on the
amount of raw data.




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    3.5 Visualizations
After the processing of the data, the pre-defined visualizations according to 2.2 will be available in
the tree view. If you click on the “+” next to “Visualization” the tree will open and show the different
categories for which plots were produced (consumption, air handling units, water circuits and
indoor climate).




Figure 37   Tree view showing the entries for the pre-defined plots.



You can see the plots for every category by clicking on the “+” signs and then clicking on one of
the available plots.




Figure 38   Example of the display of pre-defined visualization



Concerning the explanation and interpretation of the different plots, please refer to 2.2


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   3.6 Model based analysis
In order to invoke the model based analysis, choose “Define Model” from the “Model based
analysis” menu or click the model view symbol. The main window will change to the model view
which gives you the possibility to enter units and define their parameters and inputs. The tree view
will be extended by the “Outputs” window which will show the outputs of the defined units that can
be used for defining connections later on.




Figure 39   Main window with data assignment



At first you have to define the location where the building is situated or the measurements are
taken respectively, on the left side of the main window. The location has to be given in standard
absolute coordinates in degrees (Longitude (West negative, East positive) and Latitude). (A helpful
website for this is http://www.gorissen.info/Pierre/maps/googleMapLocation.php)
Furthermore, you will have to assign a couple of sensors that are needed either as input for the
model or for comparison to the results of the model. Sensors must be assigned to the following
overall model inputs or outputs, respectively:
   o   Overall model inputs:
            o   Outdoor air temperature
            o   Outdoor air relative humidity
            o   Total horizontal solar radiation
   o   Overall model outputs
            o   Energy consumption for heating
            o   Energy consumption for cooling
            o   Energy consumption for electricity

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After completing the data assignment, you have to define the model. Start with clicking the “new
component” tab on the left side of the main window.
As described in 2.3 the overall system model consists of the following components:
    o   Zone(s)
    o   Air handling units
    o   Emitter
    o   Distribution
    o   Storage
    o   Generator


In addition to these HVAC components, the following units have to be defined in order to receive a
system model that can be used to calculate the energy demand of the building:
   o    Day profiles and schedules:
        These two units are used for the definition of occupancy and operation schedules.
        “Day profile” is used to define a time profile for one day (e.g. number of persons in a zone
        on a workday).
        “Schedule” can combine two or more “Day profiles” to define more complex schedules (e.g.
        different occupancy schedules for workdays and weekends as well as for summer/winter)
   o    Splitter/merger:
        If a unit is to be connected to more than just one other unit, you will need a splitter/merger
        unit to connect them. A typical example would be a storage that is connected to several
        heat generators. Principally the Splitter/Merger unit can define a n:m connection
For all of these components or units, there is a tab in the main window under which all units of the
respective kind will be displayed in single tabs.
Principally the definition of a single unit involves the following steps:
   o    For the first unit click on the “new unit” tab in the main window. Choose the type of unit you
        want to define in the main window from the drop down list (field “Type”).
   o    Assign a name to the unit (field “Name”)
   o    Click “select” to go further with the definition of parameters and inputs for the unit.
   o    A type specific dialog will pop up in the main window where you have to enter parameters
        and inputs for the unit. For all parameters default values are assigned.
   o    After completing the unit, you can add another unit by clicking on the “New”-tab in the top of
        the main window.


The following chapter will show how the different types are defined.




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      3.6.1 Day profiles
Day Profiles are defined by entering combinations of time of the day and an associated value. An
example for an occupancy schedule is shown in the Figure below.




Figure 40   Example of the definition of a day profile for occupancy on workdays

Note: the time before the first and after the last time entries is considered to have the value of the
last time entry.
Day profiles need no input; therefore the input panel is empty.
If you click on the “outputs” button on the top of the right in the main window the outputs of the day
profile is shown. This is just for information. You can find this button in every units and it helps you
to check the outputs.




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        3.6.2 Schedules
Schedules distinguish between “active” and “inactive” periods. Fore each period you an assign a
value, a day profile or another schedule. In the main window on the left you will find fields that
allow you to enter a constant value (“Parameter”) for the active and inactive period and different
tables for date, weekday and months that are used to define the active period. Instead of the
constant values you can drag and drop one of the defined Day Profiles from the outputs window to
the input fields for active value or inactive value on the right of the main window.
The active period can be defined by entering periods of time in the Date, Weekday and Month
fields. The fields are connected by a logical AND, i.e. each entry will further constrain the active
period. The following entries are possible:
    o    Date:
         Periods defined by dates such as: 1.1.2009 – 31.3.2009
    o    Weekday:
         Either single days (e.g. “Su” for Sunday), period of days (e.g. “Mo – Fr”, for Monday to
         Friday) or selection of days (e.g. “We, Fr, Su”, for Wednesday, Friday and Sunday) can be
         entered usinf the abbreviations of the day names.
    o    Months
         Same as Weekdays but for months. Months can be written as abbreviation (e.g. Jan - Apr)
         or as number (e.g. 1-4)


The next Figure shows an example for a schedule, using day profiles as inputs to define an
occupancy schedule that differs for Workdays (active values) and Weekends (inactive values).




Figure 41    Example of a schedule for occupancy
             Note the Outputs window on the bottom of the Tree View which gives you access to the outputs of the
             units, in this case the day profiles.




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          3.6.3 Zones
The definition of a zone requires parameters to define the characteristics of the zone, inputs for
potentially adjacent zones and default values for the inputs if these are not connected to another
unit.




Figure 42      Definition of a zone. The parameters are entered on the left side of the main window; the inputs are
               defined on the right.

On the left side of the main window the parameters (“Parameters” and “Window Parameters” and
“Adjacent Zone Parameters”) as well as the default values for the inputs are entered. On the right
hand side the inputs can be connected to other units like schedules
The following Table shows the parameters and inputs of the zone model:

Table 10       Parameters and inputs of the zone model


Parameter                     Description

TZone_start                   start value for the temperature of the zone air [°C]

RHZone_start                  start value for the relative humidity of the zone air[%/100]

TZoneMass_prev                start value for the temperature of the construction mass of the zone[°C]

TAdjancentZones_prev          start value for the temperature of the adjacent zones [°C]

cp_air                        specific heat at constant pressure [kJ/(kg*K)]

rho_air                       density [kg/m3]

A_f                           conditioned floor area [m2]

V_a                           heated/cooled air volume [m3]

A_tr                          area of opaque external surfaces [m2]



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U_tr                 transmittance of external opaque surfaces (walls, roof) [W/(m2*K)]

A_ground             area of ground coupled surfaces [m2]

U_ground             U-value of ground coupled surfaces [W/(m2*K)]

f_ground             Coefficient or reduction for ground coupled surfaces [-]

Sens_people          sensible internal gain due to 1 person [W]

TZoneMass_prev       initial value for temperature of mass node of zone

THETA_az_prev        initial value for temperature of potential adjacent zones

LAMBDA_at            ratio internal surface/floor area
                     (according chapter 7.2.2.2 EN ISO 13790:2008 CAN be assumend 4.5)

Lighting_Threshold   value of global radiation below which lighting is on [W/m2]

SensPersons          sensible heat gain of 1 person [W]

Hum_people           Humidity gain of 1 person [g/h]

Window Parameter     Description

U_win                Average U-value of all windows (glazing + frame) [W/(m2*K)]

FF                   Fraction of frame related to gross window area

FC                   correction factor for angular variation of incident radiation

A_win_H              Overall area of windows with orientation: Horizontal

A_win_N              Overall area of windows with orientation: North

A_win_NE             Overall area of windows with orientation: North East

A_win_E              Overall area of windows with orientation: East

A_win_SE             Overall area of windows with orientation: South East

A_win_S              Overall area of windows with orientation: South

A_win_SW             Overall area of windows with orientation: South West

A_win_W              Overall area of windows with orientation: West

A_win_NW             Overall area of windows with orientation: North West

g_H                  area weighted mean of g_value for windows with orientation Horizontal

g_N                  area weighted mean of g_value for windows with orientation North

g_NE                 area weighted mean of g_value for windows with orientation North East

g_E                  area weighted mean of g_value for windows with orientation East

g_SE                 area weighted mean of g_value for windows with orientation South East

g_S                  area weighted mean of g_value for windows with orientation South

g_SW                 area weighted mean of g_value for windows with orientation South West

g_W                  area weighted mean of g_value for windows with orientation West

g_NW                 area weighted mean of g_value for windows with orientation North West

SF_H                 area weighted mean of shading factor for windows with orientation Horizontal

SF_N                 area weighted mean of shading factor for windows with orientation North



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SF_NE                             area weighted mean of shading factor for windows with orientation North East

SF_E                              area weighted mean of shading factor for windows with orientation East

SF_SE                             area weighted mean of shading factor for windows with orientation South East

SF_S                              area weighted mean of shading factor for windows with orientation South

SF_SW                             area weighted mean of shading factor for windows with orientation South West

SF_W                              area weighted mean of shading factor for windows with orientation West

SF_NW                             area weighted mean of shading factor for windows with orientation North West

SF_I_Threshold                    value of global radiation above which the shading is activated [W/m2]

Inputs*                           Description*

Persons                           Schedule or input for the number of persons in the zone [-]

Lighting                          Schedule or input for the electric power demand of lighting in the zone [W]

Appliances                        Schedule or input for the electric power demand of appliances in the zone [W]

TCoolSet                          Schedule or input for the set point of the cooling temperature of the zone [°C]

THeatSet                          Schedule or input for the set point of the heating temperature of the zone [°C]

VDot_air_sup                      Schedule or input for the air flow rate (with supply air temperature TAirSupply) of the zone
                                  [m³/h]

TAirSupply                        Schedule or input for the supply air temperature of the zone [°C]

RHAirSupply                       Schedule or input for the relative humidity of the supply air of the zone [%/100]
* note: if you don’t provide a schedule or link to another unit for the inputs, the default value will be used that can be changed on the left
side of the main window under “Defaults”



In order to connect a schedule to an input, drag and drop “active” entry of the respective schedule
in the Type window of the tree view into the field of the input.
If the zone is adjacent to another zone, the following additional parameters are necessary per
adjacent zone:

Table 11        Additional parameters for adjacent zones


Parameter                         Description

A_iw                              Overall area of light weight adjacent walls [m2]

U_iw                              area weighted mean of U-values for light weight adjacent walls [W/(m2K)]

A_if                              Overall area of heavy weight adjacent walls [m2]

U_if                              area weighted mean of U-values for heavy weight adjacent walls [W/(m2K)]


In order to define adjacent zones, you have to drag and drop a zone from the “Outputs” window to
the inputs field “AdjacentZones_”. You have to choose the “Zone”-output from the zone which is
adjacent to the current zone. Then you will have to specify the parametsrs of the connecting walls
in the “AdjacentZonesParameters” block on the left side of the main window.
Note: the parameters for the adjacent walls are entered in both zones automatically.



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Figure 43   Main window after a new zone (z2) was created by adding an adjacent zone to z1




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        3.6.4 System components
As described in 2.3.4 a general model for the following system components was implemented:
    o   Emitters
    o   Distribution pipes
    o   Storages
    o   Generators


All components have the same parameters as the basic model is the same (efficiency box).




Figure 44   Example of a definition window for an emitter.



An important point is the input “EnergyRequest” that has to be connected to the component that is
asking energy from the component that is currently defined. E.g. In Figure 44 an emitter is
connected to a zone which should be supplied with heat or cold by the emitter. Thus the input has
to be chosen from a unit in the direction of the energy flow.
In order to connect a unit to the “EnergyRequest” input, just “open” the respective unit in the Output
window of the tree view by clicking “+” and drag and drop the unit to the input field. After doing so
the unit type and name will appear in the input field.
Note:
All units except of the zones have an output “EnergyDemand” which can connected to the
EnergyRequest. In the case of zones you will have to choose the “Zone” Output. This
output contains the energy demand implictely.



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Table 12 gives an overview over the parameters that have to be defined for a system component.




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Table 12       Parameters and inputs of the system component model


Parameter                   Description

Control                     Default value (if no schedule is connected) for the control value.
                            the control value is between 0 and 1 and can be used to control the thermal power of the
                            component between 0% and 100%

isHeating                   flag if unit can provide heat (0 = no, 1=yes)
                            e.g. a gas boiler can provide heat, but a chiller or a cold distribution can not

isCooling                   flag if unit can provide cold (0 = no, 1=yes)

isElectric                  flag if unit is supplied / driven by electricity (0 = no, 1=yes)
                            e.g. a compression chiller or a direct electric heating. Note:

isHeatExchanger             flag if unit is an heat exchanger (0 = no, 1=yes)

isConverter                 flag if unit is converting energy (0 = no, 1=yes)
                            e.g. typically all generators (including heat exchangers,e.g. for district heat and cold) are
                            converting energy. If 0 (no converter), this is a pure loss unit, e.g. a distribution pipe

efficiency_h                Efficiency of heat production (0-1 [-])

k_h                         Conversion factor for auxiliary electrical energy (0-1) see 2.3.4 [-]

W_aux_h                     Auxiliary electrical energy for component [W]
                            e.g. the electric power consumption of a gas boiler or absorption chiller

W_aux_h_load_dep            flag if auxiliary electrical energy is linearly dependent on the thermal load of the unit (0 = no,
                            1=yes). If 0 W_aux_h will be constantly consumed if unit is operated

Pmax_h                      nominal power of component [W]
                            If “-1” is entered the nominal power will be dependent on the unit that provides power to
                            the current unit (that is e.g. the case for storages or distribution pipes)

efficiency_c                Efficiency of cold production (0-1 [-])

k_c                         Conversion factor for auxiliary electrical energy (0-1) see 2.3.4 [-]

W_aux_c                     Auxiliary electrical energy for component [W]
                            e.g. the electric power consumption of a gas boiler or absorption chiller

W_aux_c_load_dep            flag if auxiliary electrical energy is linearly dependent on the thermal load of the unit (0 = no,
                            1=yes). If 0 W_aux_h will be constantly consumed if unit is operated

Pmax_c                      nominal power of component [W]
                            If “-1” is entered the nominal power will be dependent on the unit that provides power to
                            the current unit (that is e.g. the case for storages or distribution pipes)

Inputs                      Description

Control                     schedule for the control of the unit
                            the control value has to be between 0 and 1 and can be used to control the thermal power
                            of the component between 0% and 100%

Energy request              energy requested by the next unit (in direction of energy flow) from the current unit




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In order to make the parameters of the system component model more clear the next table gives
some examples.

Table 13       Parameters and inputs of the system component model


Parameter             unit    Gas Boiler   Compression      District   reversible        heat          floor
                                             chiller         Cold      heat pump     distribution    heating /
                                                                                                      cooling

Control                 -         1              1             1           1              1             1

isHeating               -         1              0             0           1              1             1

isCooling               -         0              1             1           1              0             1

isElectric              -         0              1             0           1              0             0

isHeatExchanger         -          -             -             1           -               -             -

isConverter             -         1              1             1           1              0             0

efficiency_h            -         0,9            0             0          4,0             0,9          0,96

k_h                     -         1              0             0           0              1             0

W_aux_h                W          50             0             0           0              30            0

W_aux_h_load_dep        -         0              0             0           0              1             0

Pmax_h                 W        15000            0             0         20000            -1            -1

efficiency_c            -         0             3,5           0,9         3,0             0            0,95

k_c                     -         0              0             0           0              0             0

W_aux_c                 -         0              0            100          0              0             0

W_aux_c_load_dep                  0              0             1           0              0             0

Pmax_c                 W          0            30000         20000       18000            0             -1




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          3.6.5 Air Handling Units (AHUs)
All types of air handling units (see 2.3.3) are defined with the same definition window. The
functions of the AHUs are chosen by setting the parameters accordingly (e.g. if the AHU does not
comprise a cooling coil the maximal air flow rate in cooling mode is set to 0).




Figure 45      Example of a definition window for an AHU.




Table 14       Parameters and inputs of the system component model


Parameter                    Description

Control                      Default value (if no schedule is connected) for the control value.
                             The control value is between 0 and 1 and is used to control the air flow rate of the AHU.
                             between 0% and 100% [-]

TZone_start                  start value for the air temperature of the supplied zone [°C]

RHZone_start                 start value for the relative humidity of the air of the supplied zone [%/100]

TZoneMass_prev               start value for the construction mass temperature of the supplied zone[°C]

Ta_start                     start value for the outdoor air temperature [°C]

R_start                      start value for the outdoor air relative humidity [%/100]

VDot_hyg_max                 hygienic air flow rate at design conditions [m³/h]

VDot_heat_max                maximal air flow rate in heating mode [m³/h]

VDot_cool_max                maximal air flow rate in cooling mode [m³/h]

T_sup_h_max                  maximal supply air temperature in heating mode [°C]


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T_sup_h_min      minimal supply air temperature (pre-heating) [°C]

T_sup_c_min      minimal supply air temperature in cooling mode [°C]

T_sup_c_max      maximal supply air temperature (pre-cooling) [°C]

T_mean_w         mean temperature of water in cooling coil [°C]

SFP              specific fan power at design air flow rate [W/(m³/h)]

k_h_fan          Conversion factor for electric power of the fans (i.e. part of the electric power that is
                 converted to thermal power that will increase the supply air temperature, value 0-1, similar to
                 conversion factor of system components, see 2.3.4 [-]

P_el_aux_HC      auxiliary electric power for heating Coil (e.g.for a secondary pump) [W]

P_el_aux_CC      auxiliary electric power for cooling Coil (e.g.for a secondary pump) [W]

Eff_HRC          Efficiency of hear recovery (part of the maximal enthalpy difference that is recovered,
                 value 0-1) [-]

P_atm            atmospheric pressure, default is 1 [atm]

Inputs           Description

Control          schedule for the control of the unit
                 the control value has to be between 0 and 1 and can be used to control the air flow rate of
                 the AHU. between 0% and 100%. [-]

Supplied Zone    Zone that is supplied by the AHU
                 In order to connect a zone, drag and drop the “Zone” output of the respective zone from the
                 Outputs window to this input. A AHU can only supply ONE zone.

Energy request   energy demand (or fraction of energy demand if another emitter, e.g. a radiator is present) of
                 the supplied zone.
                 NOTE:
                 If a zone is supplied with heat / cold ONLY by the AHU, you can drag and drop the “Zone”
                 output of the respective zone from the Outputs window to this input. If the zone is supplied
                 with heat / cold also by another emitter (e.g. a radiator or fan coil unit), the emitter and the
                 AHU has to be connected to the zone using a SpliiterMerger unit (see 3.6.6). In this case one
                 of the ouputs of the “EnergyDemand” outputs of the SplitterMerger unit has to be dragged
                 and dropped to this input.




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      3.6.6 Splitter/merger
In order to allow for complex structures there must be the possibility to connect more than one unit
to one or more other units. In reality this is done in the hydronic system via diverting or mixing
devices. The corresponding unit in the model is called Splitter/Merger that allows an n:m
connection.




Figure 46   Example for a SplitterMerger connecting 2 generators with 3 distribution pipes
            (the arrow show the direction of information flow which is opposite to the energy flow)

The SplitterMerger sums up the energy demand of the components connected to the inputs. The
components connected to the outputs can either be controlled in series or parallel by setting the
“type” parameter.
In “series” mode (type = 1) the total energy demand of the input units will be supplied by the
component that is connected to the first output of the SplitterMerger as long as its capacity is
sufficient. If the total energy demand exceeds the capacity of the component at the fist output, the
component at the second ouput will provide the extra power (if its capacity is sufficient), and so on.
In “parallel” mode (type = 0) the total energy demand is distributed in equal parts to the
components connected to the outputs.
An example would be a storage that is supplied with heat by two gas boilers in series mode. In this
case the energy demand of the storage (“EnergyDemand” output of the storage) has to be
connected to the first input of the splitter merger. The numberOfOutputs parameter of the
SplitterMerge has to set to 2. Then the first “EnergyDemand” output has to be connected to the gas
boiler with priority 1, i.e. that is to be used first and the second ouput has to be connected to output
2.




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Figure 47   Definition window of a SplitterMerger. In this example the SplitterMerge connects one zone (z1) to an
            emitter and an AHU. The Energy demand of the zone is connected to the EnergyRequest input of the
            SplitterMerger and for its outputs the numberOfOutputs parameter has to be set to 2.



In order to define a SplitterMerger unit you will have to define the number of outputs on the left side
of the main window. Outputs in this case mean the number of units that provide energy to the
SplitterMerger.
For the inputs you will have to drag and drop the outputs of the respective units from the Outputs
window of the tree view to the input field of the SplitterMerger. If one input is connected the next
input field will be created automatically.
The outputs of the SplitterMerger can then be connected to the units that provide energy to the
SplitterMerger. In order to do this you have to drag and drop the outputs of the SplitterMerger from
the Outputs window of the tree view to the input field (EnergyRequest) of the respective unit.

Table 15    Parameters and inputs of the Splitter/Merger unit


Parameter                  Description

numberOfOutputs            number of outputs of the SplitterMerger unit. This number corresponds to the number of
                           components delivering enery to the SplitterMerger (see examples above) [-]

type                       The “type” parameter defines if the load that is propagated to the outputs of the
                           SplitterMerger will be equally distributed (weighted by the capacity of the connected units) or
                           according to priorities (i.e. the first output will take all the load as long as its capacity is
                           sufficient, then the second will be used, and so on). For the parallel handling of the outputs
                           the “type”-parameter has top set to “0”, for the sequential or prioritized handling it gas to
                           be set to 1. [-]

Inputs                     Description



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EnergyRequest_0           energy requested of the next unit(s) (in direction of energy flow) from the current unit
                          NOTE:
                          If you drag and drop the EnergyDemand ouput of a unit to this field, an additional field
                          (EnergyRequest_1) will automatically be added to the list so that a second input can be
                          connected if necessary.



      3.6.7 Cross table view
The “CrossTable” tab in the main window provides an overview over the connection of the units in
form of a cross table. All units are listed in vertical as well as in horizontal direction. The
connections between the units are denoted with arrows.




Figure 48   The main window with the cross table view, showing the connection of the units of the system



The arrows also give the “direction” of the link. The energy demand of the unit in the row of the
arrow is supplied by the units in the column of the row. Double clicking on an arrow or a unit shows
the links in detail (single input/output statements).




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      3.6.8 Invoking the calculation / inspect results
In order to start the calculation go to the “ResultUnits” tab.




Figure 49   The definition window with the definition of a result unit

Drag and drop all outputs of interest from the Outputs window to the input fields of the result unit. O
the left side you can specify scale factors, if e.g. units have to be converted. Furthermore you can
select and deselect the single outputs in the ShowResult list in order to display them in the result
plot or not.
In the bottom of the result unit you must specify the period of time for which the calculation should
be performed. Please assure that the measured data (before all outdoor air temperature, solar
radiation and outdoor air relative humidity) are valid during that period.
To start the calculation press the button apply in the lower right. Depending on the complexity of
the system and on the length of the period the calculation can take up to several minutes.




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When the calculation is finished a result window will pop up. You can choose to look at the results
in a data table or as a time series visualization.




Figure 50   The results are displayed eother as a plot or as a data table

The plots are sorted in the following order (from bottom-up):
    o   Energy demand of all generators (cold and Heat) and sum of their outputs.
        This will be equal to the sum of the measurements of the end energy for heating and
        cooling.
    o   Electricity demand
        The electric energy demand of all zones, AHUs and the total electric energy demand
    o   Cold
        The cold demand of all zones and generators
    o   Heat
        The heat demand of all zones and generators
    o   Others
        all results and measured values you might have assigned to the result unit and that do not
        fit to any of the other plots are displayed in this plot.


Note: you can define more than one result unit, if you want to look at a significant number of
results.




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      3.6.9 csv-export of data
In order to be able to examine the results in another software, you can export selected outputs as a
csv-file (ASCII format).




Figure 51   definition window of the csv-export

Cretae a new csvWriter by clicking the “new” tab. Then drag and drop all outpts you want to export
from the Outputs window to the input list of the CSVWriter unit. For every output you can define a
scale factor on the left side of the main window if necessary.
You then have to define a file name (either enter name or double click to select with dialog) and a
separator. After choosing a period of time and clicking the “Apply” button the results will be
exported

    3.7 Automated Outlier detection
The automated outlier detection is divided in a training phase and a application phase according to
2.4. You can access both phases via the “Outlier Detection” option in the menu of the tool. First the
training period has to be chosen and the training will be performed. After the training is completed,
you can choose a period for the application and start the analysis. The result is presented in a plot
that shows the outliers found during the application phase.
[Note: At the time of editing this report, this feature is not yet accessible via the GUI. Please check
www.buildingeq.eu under the link “results” for updates.




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    4 National tools for performance analysis of buildings
As part of the project national existing tools have been enhanced by the industrial Partners
(ennovatis and Granlund) incorporating hints coming out of the common developing work . Such
tools are more country specific and sophisticated than the developed European tool described in
the preceding paragraphs. A short description follows.

    4.1 German: ennovatis tool
Ennovatis has developed two main tools that use basically the same data structure but differ for
the calculation time step, .EnEV+, which works on monthly averaged days, i.e. monthly based, and
VEC, which is hourly based.

       4.1.1 Structure of the tools
The overall structure of the ennovatis tools is shown in Figure 52.




Figure 52: Structure of the ennovatis software



The software is component based. The components cover all the requirements stated in the
previous paragraphs, i.e. according to the developed basic European tool . It may be used in
different contexts including the one to support the evaluation process developed in the present
project and reported in “Requirements for data and measurement equipment: Guidelines for the
evaluation of building performance”.



       4.1.2 Data handling
All data are stored as abstract data types in a hierarchical structure which can be seen in Figure
53. Substructures exist for basic descriptions of the building its usage and its location, the
architectural model and various thermal models derived from it to allow specific engineering
investigations, the description of the HVAC system, the description of measurement points and the
calculated and measured results.


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Figure 53: The hierarchical data model of the ennovatis tool set

Most data are stored in a relational data base. However special treatment is given to the abstract
data type time series. The data management system uses netCDF, an optimized binary format to
store time series from both measurements and simulations and a relational databank system for
the management of meta-information.
For data acquisition the data management provides the following basic functionalities
                1. Flexible driver concept.
                2. Automatic, time controlled or manual actualisation of both measured and
                   calculated data.
                3. Real time data base without intermediate tables
                4. Administration of all data and actions.
                5. Data conversion (units or statistical values)

       4.1.3 Data Visualization
Visualisation components exist for all data types available in the system. The presentation modes
developed in Building EQ were implemented for the tool too. In addition a web based version of the
result presentations is available. It was used to present various demonstration buildings including
the multi purpose building of the university of Stuttgart on the ennovatis energy management demo
page http://demoportal.ennovatis.de/eng/index.php
Also energy certificates according to the German rules and/or energy reports can be generated on
an automatic basis.




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     4.1.4 Graphical User interface
The Graphical User Interface supports working with the system during all passes of the building life
cycle. This includes:
              • Generation and/or visualisation of the architectural model in 2D and 3D
                presentations
              • Input of all data to perform a building simulation on a yearly, monthly or hourly
                basis
              • Graphical zoning of the architectural model to generate different thermal models
              • Visualisation of time series from simulation or measurement in all modes
                described in the WP 3 report
              • Generation of energy reports including recommendations for energy saving
                measures
              • Generation of energy certificates according to the German implementation of the
                EPBD
Samples of visualisations can be found throughout this report and on our energy management
demo portal http://demoportal.ennovatis.de/

     4.1.5 Method of analysis
Besides graphical and statistical methods for analysis the ennovatis tools also provide various
simulation kernels: EnEV+, which works on monthly averaged days, i.e. monthly based, and VEC,
which is hourly based. Of course this switching between calculation kernels requires some
additional input which mainly reflects the higher time resolution. This is realised in the user
interface.
The monthly based calculation kernel (EnEV+) is based on the German implementation of the
EPBD which is called DIN V 18599. Of course it is CEN conform. It was implemented by
Fraunhofer IBP and will be used in various national and international projects as basic calculation
method.
The main features of this holistic approach are
                      monthly calculation
                      (nearly) any system technology can be calculated, also combined systems
                      building has to be divided into usage zones
                      standard user profiles for different parts
                      iterations necessary
Due to the integration of the kernel into the ennovatis environment three types of calculations are
possible. They include
                      Norm calculation which uses assumptions and rules from DINV18599
                      Reference calculation which uses building with standardized material and
                      system data
                      Individual calculation which considers building specific parameters and
                      operation conditions
The hourly based calculation kernel (VEC) is instead based on VDI 2067 German standards.

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      4.1.6 Validation
Validation has been done against both measurements and more detailed simulations. The
validation against measurements will be reported in more detail in conjunction with the
demonstration projects in the “Implementation in the demonstration buildings and continuous
assessment of performance” report.
Calculations were performed primarily for the ennovatis office building in Großpösna. The building,
its technical equipment and the consumption data are described in more detail in our energy
management portal http://demoportal.ennovatis.de/.
The ennovatis office building is a small and simple building which consists of two floors.
Characteristic data are

          location:                     D-04463 Großpösna near Leipzig

          weather data:                 arithmetic mean of ten years for Leipzig 8,7 °C

          year of construction:         1996

          net base area:                436 m²

          gross floor area:             806 m²

          use:                          office, workshop

          miscellaneous:                vacancy 2004 - 2006

Data of the energy consumption - 2005-2007:

         medium                         10-12/05               2006              2007

         gas [kWh]                      5.670                  39.208            36.076

         electricity [kWh]              1.544                  8.993             15.377

         water [m³]                     6,2                    32,5              46,1



Geometry and major models are given in Figure 54




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View of the Test Building                              3D Model for the ennovatis Tool




2D Model and Zoning                                    3D Model for Energy+


Figure 54 Geometry and Modelling of the Testbuilding Großpösna

The example building has a simple heating system in place that provides heating mostly during the
winter. There is no cooling available for this building. The heating system was implemented with
baseboard heaters on the zone level and a hot water plant loop providing heating energy through a
boiler. For measuring purposes the minimal data set was implemented.
Nevertheless the building does not fulfil the requirements of being a demonstration building for
Building EQ, but can serve to validate some of the Building EQ tools and theories in a transparent
way.
Simulations were performed using the ennovatis tool EnEV+ (DIN V 18599) and the US tool
EnergyPlus (E+). The following Figure 55 and Figure 56 compare results for heating energy
demand and primary energy demand on a yearly and a monthly base.




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                                                 Primary energy demand
                                                 DIN V 18599      E+


                                Heating energy demand
                                DIN V 18599     E+




Figure 55: Comparison of EnEV+ and E+ calculations yearly data




Figure 56: Comparison of EnEV+ and E+ calculations monthly data

In general EnergyPlus provides more input parameters than the DIN V 18599, thus a variety of
parameters were set to their default values. While the overall results show a good agreement for
this building, it has to be verified if the default values reflect the assumptions for the DIN V 18599
in general. Figure 55 illustrated the annual comparison of demand heating energy and primary
(supply) energy usage at the plant. The EnergyPlus values are slightly bigger than the ones from
DIN V 18599. The differences increase if we go to monthly data as shown in Figure 56. Of course
in this case the assumptions made for the different simulations (for example for February or
October) show also greater differences.
Similar validation has been also performed for the hourly based calculation kernel VEC.




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       4.1.7 Application to Fault Detection and Diagnosis
EnEV+ calculates demand on a monthly basis. This is very crude from the point of view of fault
detection and diagnosis (FDD); therefore VEC is preferred FDD being based on hourly
calculations.
The VEC package contains a software component called visual data analyser. It allows to combine
and to treat data from different sources in different ways to derive new information and to initiate
appropriate actions. ‘The basic idea is shown in Figure 57: The visual data analyser supports
intelligent metering and rule based monitoring. (blue marks time series to be compared, red stands
for the comparison procedure and green for possible reactions like messages to service personnel
or marking of data as quality assured)




Figure 57: The visual data analyser supports intelligent metering and rule based monitoring

This concept is used in different ways to perform FDD
   • Intelligent metering
                           people view data with high time resolution in various contexts
   • Rule based monitoring
                           Derive rules how to interpret consumption data from intelligent metering
                           Apply soft- or hardware to fire rules
   • Model based monitoring
                           Determine actual demand from process model
                           Derive rules how to interpret consumption data from demand calculations
                           Apply software to fire rules
To perform model based monitoring the concept of the visual data analyser had to be developed
further. This is shown in Fehler! Verweisquelle konnte nicht gefunden werden.




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Figure 58: The concept of model based monitoring




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    4.2 Finland: Granlund tool
Grandlund has developed a suit of tools which are covering almost all the huge field of buildings,
form services design to information management, from building performance evaluation and
assessment to maintenance management. To interoperate such tools effectively for continuous
commissioning application a Building Performance Information System (BPIS) has been developed
and used: Taloinfo Facility Reporting.

       4.2.1 Structure of the tool Taloinfo




Figure 59: The structure of Taloinfo



Taloinfo is a new kind of Building Performance Information System. It collects real-time data from
different sources such as building automation, access control, energy metering and maintenance
systems (RYHTI). Taloinfo turns the data into visual reports that can be used to track the facility’s

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performance and analyse it from different viewpoints. Taloinfo also simulates targets for indoor air
conditions and energy consumption with the help of the RIUSKA energy&thermal simulation
software and the building product model (i.e. the 3D Building Information Model – BIM).
Taloinfo is intended as a reporting tool for facility users and management. Taloinfo is the first step
towards comprehensive facility information management – the self-reporting building.
Taloinfo has been implemented for instance at the headquarters of Senate Properties in Helsinki,
one of the monitored building in the project.




Figure 60: The structure of Taloinfo



In Figure 60 the main functional boxes are shown. These functionalities are normally provided by
different hardware and software programs or packages; Taloinfo is just effectively interoperating
such different software to provide a tool which allows an integrated building performance
assessment and optimization.




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The top level of its functionalities is the executive reporting which is performed by the software
RYHTY.


                                    RYHTI™ is a comprehensive solution for facility maintenance
                                    management, containing the tools for everything from
                                    management reporting to design and monitoring of functions.
                                    RYHTI is intended for use by all user levels and groups, from
                                    facility management to maintenance staff and service providers.
                                    The software can be used to manage property portfolios as well
                                    as individual buildings, new constructions as well as existing
                                    facilities.
                                  In maintenance management RYHTI is used to maintain up-to-
                                  date facility information, and to plan and follow through on
maintenance and service actions. It is also possible to plan and budget repair work, handle
problem reports and requests for service, monitor consumption etc.
For maintenance management reporting its extension, the RYHTI Executive software package is
used. It offers all-new information management solutions for facility management, making use of
information contained in RYHTI databases and other information systems related to maintenance
management and the facility.
The reporting can be at different levels according to the user level and the requirements:
   1. Performance Management:
energy reporting of different consumption, components reporting of conditions
   2. Energy Certificate:
support consumption classification based on energy certificate
           -   total consumption of cooling /heating
           -   measurement of lighting (support for energy certificate's own classification of
               electricity)
   3. Baseline:
current measurement practice; support mainly for energy billing:
           -   total consumption of heating/cooling
           -   total consumption of electricity
           -   total consumption of water.
To simulates targets for indoor air conditions and energy consumption (i.e. to define comfort and
energy benchmark), the comfort and energy simulator RIUSKA is interoperate on the background
(Figure 61).




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Figure 61: Interoperation among Riuska, 3D BIM and Taloinfo programs




█   Other electricity
█   Equipment electricity
█   Lighting electricity
█   Fan electricity
█   Cooling electricity
█   Heating energy




Figure 62: Riuska program

RIUSKA™ is a tool for the dynamic simulation of comfort and energy consumption in building
services design and facilities management. It simulates thermal conditions and heating and cooling
of individual spaces and can be used to compare and dimension HVAC systems as well as for
calculating the energy consumption of whole buildings. RIUSKA covers all the requirements of
thermal performance simulation from preliminary design to facilities management and renovation. It
has been developed specifically as a practical design tool for use by engineers in their everyday
work. RIUSKA is based on many years of development work at Granlund an its engine is the
internationally known DOE 2.1 E simulation program, and will be upgraded soon with EnergyPlus.



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       4.2.2 User interface and data visualization
The user interface, which is mainly addressed to the reporting issue, has several levels according
to the user’s expertise. Its allows a very simple and immediate information transfer via visual
images (Figure 63) on overall building performances, both on comfort and on energy for inexpert




Figure 63 – Inexpert user interface

But it can also provide more detailed information for expert users (Figure 64 and Figure 65), where
the data visualization is based on comfort related reports or on energy related reports.




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Figure 64 – Visualisation of data by comfort related reports




Figure 65 – Visualisation of data by energy related reports




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Figure 66 – Visualisation of 3D building maps and performance maps

Taloinfo graphic interface uses instead a 3D BIM (Building Information Model) for the data input, to
navigate by geometry through the building for localizing and visualising comfort zones or
discomfort zones, service systems set points and measured ambient quantities as temperatures,
humidity, air quality etc., as shown in Figure 66.




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Annex




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Unified point naming convention
Table 16 shows the point naming for the minimal data set. Table 17 gives the abbreviation used in Table 16.

Table 16       point naming for the minimal data set
               items in brackets () denotes possible choices for extensions of an abbreviation which are used for further specification.
               items in squared brackets [] are user defined. As extensions of the abbreviations they are optional


data point of minimal data set        Categries

                                      Building     Zone      System                  Subsystem1                 Subsystem2      Medium             Position       Kind        Point

total consumption of district heat    [BUI]        WBD       DH                      MTR.H                                      HW                                MEA         E.H

total consumption of district cold    [BUI]        WBD       DC                      MTR.C                                      CHW                               MEA         E.C

total consumption of fuels            [BUI]        WBD       ESUP                    MTR                                        FUEL                              MEA         E

total consumption of electricity      [BUI]        WBD       ESUP                    MTR.EL                                                                       MEA         E.EL

total consumption of water            [BUI]        WBD       WSUP                    MTR.W                                      DCW                               MEA         VOL

outdoor air temperature               [BUI]        WBD       WTH                                                                OA                                MEA         T

outdoor air rel. humidity             [BUI]        WBD       WTH                                                                OA                                MEA         RH

global irradiation                    [BUI]        WBD       WTH                     GLOBSENS                                                                     MEA         SOL

supply temperature of water circuit
for heating, cooling or both          [BUI]        [ZONE]    WC.(H,C,HC).[Name]                                                 (HW,CHW)           SUP.(PRIM,SEC) MEA         T

return temperature of water circuit
for heating, cooling or both          [BUI]        [ZONE]    WC.(H,C,HC).[Name]                                                 (HW,CHW)           RET.(PRIM,SEC) MEA         T

set value/signal of control signal/
status of pump of water circuit       [BUI]        [ZONE]    WC.(H,C,HC).[Name]      PU.[number]                                                   (PRIM,SEC)     (SEV,SIG)   (CTRLSIG,STAT)

indoor air temperature of [ZONE]      [BUI]        [ZONE]                                                                       RA                                MEA         T.[number]

indoor air rel. humidity of [ZONE]    [BUI]        [ZONE]                                                                       RA                                MEA         RH.[number]

supply air temperature of
AHU.[number] that servers [ZONE]      [BUI]        [ZONE]    AHU.[number]                                                       SUPA                              MEA         T

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exhaust air temperature of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]                                        EXHA                          MEA         T

supply air rel. humidity of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]                                        SUPA                          MEA         RH

exhaust air rel. humidity of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]                                        EXHA                          MEA         RH

set value/signal of control signal/
status of supply fan of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]   FAN                                  SUPA                          (SEV,SIG)   (CTRLSIG,STAT)

set value/signal of control signal/
status of exhaust fan of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]   FAN                                  EXHA                          (SEV,SIG)   (CTRLSIG,STAT)

supply temperature of heating,
cooling, pre-heating or pre-
heating/cooling coil of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]   (HC,CC,PREHC,PREHCC)                 (HW,CHW,HCW)   SUP.(PRIM,SEC) MEA         T

return temperature of heating,
cooling, pre-heating or pre-
heating/cooling coil of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]   (HC,CC,PREHC,PREHCC)                 (HW,CHW,HCW)   RET.(PRIM,SEC) MEA         T

set value/signal of control signal/
status of pump of heating, cooling,
pre-heating or pre-heating/cooling
coil of AHU.[number] that servers
[ZONE]                                [BUI]   [ZONE]   AHU.[number]   (HC,CC,PREHC,PREHCC)   PU.[number]   (HW,CHW,HCW)   (PRIM,SEC)     (SEV,SIG)   (CTRLSIG,STAT)

set value/signal of control signal/
status of control valve of heating,
cooling, pre-heating or pre-
heating/cooling coil of
AHU.[number] that servers [ZONE]      [BUI]   [ZONE]   AHU.[number]   (HC,CC,PREHC,PREHCC)   CTRV          (HW,CHW,HCW)   (PRIM,SEC)     (SEV,SIG)   (CTRLSIG,STAT)




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Table 17        Possible category items and abbreviations


           No   Category         abbreviation               item

           1    Building

                                 free choice                building name

           2    Zone

                                 WBD                        Whole Building

                                 free choice                all other zones

           3    System

                                 DH                         District heat

                                 DC                         District cold

                                 ESUP                       Energy supply

                                 WSUP                       Water supply

                                 WTH                        Weather Station

                                 WC.H                       heating circuit (water circuit for heating)

                                 WC.C                       cooling circuit (water circuit for cooling)

                                 WC.HC                      heating / cooling circuit (water circuit for heating / cooling)

                                 AHU                        AHU

           4    Subsystem_1

                                 MTR                        Meter for Fuel

                                 MTR.H                      heat meter

                                 MTR.C                      cold meter

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                  MTR.EL            electricity meter

                  MTR.W             water meter

                  GLOBSENS          Pyranometer

                  PU                Pump

                  FAN               Fan

                  HC                heating coil

                  CC                cooling coil

                  PREHC             pre-heating coil

                  PREHCC            pre-heating/cooling coil

5   Subsystem_2

                  PU                Pump

                  CTRV              Control valve

6   Medium

                  HW                hot water

                  CHW               chilled water

                  HCW               hot / chilled water

                  DCW               domestic cold water

                  FUEL              any kind of fuel (gas, oil, wood, etc.)

                  OA                outdoor air

                  RA                room air

                  SUPA              supply air

                  EXHA              exhaust air

7   Position

                  SUP.(PRIM, SEC)   supply (on primary or secondary side of system)

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                                                                                             Building EQ – Tool Description



                RET.(PRIM, SEC)   return (on primary or secondary side of system)

                PRIM, SEC         primary or secondary side of system

8   Kind

                MEA               measured value

                SEV               set value

                SIG               signal (feedback from component)

9   datapoint

                E                 energy

                E.H               heating energy

                E.C               cooling energy

                E.EL              electric energy

                VOL               Volume

                T                 temperature

                RH                relative humidity

                SOL               solar irradiation

                CTRLSIG           control signal (continuous 0-100%))

                STAT              status (1/0)




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Background on implementation
The implementation of the tool is done in the scripting languages python (www.python.org) and R
(www.r-project.org) both of which are open source projects. An Overview over the used python
packages is below.
The measured data is stored in HDF5 format (Hierarchical data format) which allows to store large
amounts of data together with meta data and a given structure (http://hdf.ncsa.uiuc.edu/HDF5/).
The structure can comprise several hierarchical layers like projects, sub_projects and
sensorgroups. The sensorgroups are the last hierarchical layer as they contain actually the
measured data. An example of the general structure is given in the figure below.



             db (/)                                                    db (/)

                      project                                                   building_1

                                Sensorgroup_1                                                Raw_data

                                           Sensor_1                                                      Ta

                                           Sensor_2                                                     Iglob


                                           Sensor_n                                               Gas consumption

                                Sensorgroup_2                                            Sampled Data




Figure 67   General structure (left) and example (right) of stored measured data.



The visualization is implemented by making use of the python package “matplotlib”. This package
provides all necessary functions for 2D plotting and. Generally, the tool makes use of several
python packages that are listed below.


             Package               description                                    download
             base packages, data handling and computing

             Python 2.5.2          This is the Python 2.5 base package for        http://www.python.org/download/
             Windows               Windows
             installer
             (python-
             2.5.2.msi)

             SciPy 0.6.0 for       Library for mathematics, science, and          http://www.scipy.org/Download
             Python 2.5            engineering. SciPy depends on NumPy (see
             (scipy-               below)
             0.6.0.win32-
             py2.5.exe)
             NumPy 1.1.1           Numpy provides convenient and fast N-          http://www.scipy.org/Download
             for Python 2.5        dimensional array manipulation.
             (numpy-
             1.1.1.win32-

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py2.5.msi)

tables 2.0.3        package for managing hierarchical             http://www.pytables.org/download/sta
for Python 2.5      datasets and designed to efficiently and      ble/
(tables-            easily cope with extremely large amounts
2.0.3.win32-        of data (in HDF5 format which is used in
py2.5.exe)          datastorage).

Packages for 2D plotting and graphics

matplotlib          2D plotting library which produces            http://matplotlib.sourceforge.net/
0.91.4              publication quality figures                   or
for Python 2.5
                                                                  http://sourceforge.net/project/showfile
(matplotlib-                                                      s.php?group_id=80706
0.91.4.win32-
py2.5.exe)

PyQt v4 GPL         PyQt is a set of Python bindings for          http://www.riverbankcomputing.co.uk/
for Windows         Trolltech's Qt which is a cross-platform      pyqt/download.php
(PyQt-Py2.5-        application development framework
gpl-4.3.3-2.exe)    (is used for the interactive plotting
                    features of matplotlib, see above)

Python Imaging      The Python Imaging Library (PIL) adds         http://www.pythonware.com/products
Library 1.1.6 for   image processing capabilities to your         /pil/
Python 2.5          Python interpreter
(PIL-               (is used for generation of image files from
1.1.6.win32-        matplotlib)
py2.5.exe)




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