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					MCP: Pitfalls & Common Mistakes

Dr Jeremy Bass & RES Wind Analysis Teams (UK & USA)
SENIOR TECHNICAL MANAGER

AWEA Wind Resource & Project Assessment Workshop
30 September – 1 October 2009, Minneapolis, MN, USA




                                                      1
OVERVIEW – What Do You Need for MCP?




high quality (target) site data


      high quality, appropriately chosen,
      reference site data


            MCP software


                  the skills, knowledge & experience
                  to use software and interpret results

                                                          2
OVERVIEW – What Do You Need for MCP?




high quality (target) site data


      high quality, appropriately chosen,
      reference site data


            MCP software


                  the skills, knowledge & experience
                  to use software and interpret results

                                                          3
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS - HARDWARE


Need to avoid instrumentation issues, including:
• poor mast installation
• poor mounting of instruments (IEC; stub mounting)
• poor choice of instruments (anemometers, vanes etc)
• poor choice of data logger and/or configuration
• insufficient power!
• poor/lack of maintenance & record keeping



                              INSERT              INSERT     INSERT
                              IMAGE               IMAGE      IMAGE


                                                                      4
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS - HARDWARE


Need to avoid instrumentation issues, including:
• poor mast installation
• poor mounting of instruments (IEC; stub mounting)
• poor choice of instruments (anemometers, vanes etc)
• poor choice of data logger and/or configuration
• insufficient power!
• poor/lack of maintenance & record keeping



                              INSERT              INSERT     INSERT
                              IMAGE               IMAGE      IMAGE


                                                                      5
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 1




                                                                          Bin Averaged Ice-Free Wind Speed Ratio (Unheated/Heated) on SWEhrnM178

                                                            2


                                                           1.8


                                                           1.6
                       Windspeed Ratio (Unheated/Heated)




                                                           1.4


                                                           1.2


                                                            1


                                                           0.8


                                                           0.6


                                                           0.4


                                                           0.2


                                                            0
                                                                 0   30    60       90      120      150       180         210   240    270        300   330   360
                                                                                                      Wind Direction Bin (°N)




                                                                                                                                                                     6
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 2




                                                                        7
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 2




                                                                        8
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 2




                                                                        9
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 2




                                                                        10
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 2




                                                                        11
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – QUALITY CONTROL - 2




                                                                        12
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – UNDERSTANDING (HARD)




                                                                         13
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – UNDERSTANDING (HARD)




                                                                         14
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – UNDERSTANDING (HARD)




                                                                         15
1. TARGET SITE DATA: PITFALLS & COMMON PROBLEMS – UNDERSTANDING (HARD)




                                                                         16
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – BACKGROUND


• the fundamental principle of MCP is that site climatology, over a 20 – 25
  life, is stationary, i.e. statistics consistent over time
• reference site data must be consistent with this requirement – essential!
                                           Monthly Mean Wind Speeds at Helsingborg, Sweden: 1996 - 2007
                                      6
                                                                               Monthly means
                                                                               12 month rolling
                                                                               Linear (Monthly means)


                                      5
     Monthly Mean wind speed / ms-1




                                      4




                                      3




                                      2
                                          Jan-96



                                          Jan-97



                                          Jan-98



                                          Jan-99



                                          Jan-00



                                          Jan-01



                                          Jan-02



                                          Jan-03



                                          Jan-04



                                          Jan-05



                                          Jan-06



                                          Jan-07
                                          Apr-96
                                           Jul-96
                                          Oct-96

                                          Apr-97
                                           Jul-97
                                          Oct-97

                                          Apr-98
                                           Jul-98
                                          Oct-98

                                          Apr-99
                                           Jul-99
                                          Oct-99

                                          Apr-00
                                           Jul-00
                                          Oct-00

                                          Apr-01
                                           Jul-01
                                          Oct-01

                                          Apr-02
                                           Jul-02
                                          Oct-02

                                          Apr-03
                                           Jul-03
                                          Oct-03

                                          Apr-04
                                           Jul-04
                                          Oct-04

                                          Apr-05
                                           Jul-05
                                          Oct-05

                                          Apr-06
                                           Jul-06
                                          Oct-06

                                          Apr-07
                                           Jul-07
                                          Oct-07
                                                                Date
                                                                                                          17
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – BACKGROUND


• the fundamental principle of MCP is that site climatology, over a 20 – 25
  life, is stationary, i.e. statistics consistent over time
• reference site data must be consistent with this requirement – essential!




                                                                              18
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – BACKGROUND


• the fundamental principle of MCP is that site climatology, over a 20 – 25
                           Frequency Distribution Comparison (Period 1)
  life, is stationary, i.e. statistics consistent over time
• reference site data must be consistent with this requirement – essential!
                     20.0
                 No Observations
                     18.0
                                   16.0
                                   14.0
                                   12.0
                                   10.0
                                    8.0
                                    6.0
                                    4.0
                                    2.0
                                    0.0
                                           0.0
                                           0.5
                                           1.0
                                           1.5
                                           2.0
                                           2.5
                                           3.0
                                           3.5
                                           4.0
                                           4.5
                                           5.0
                                           5.5
                                           6.0
                                           6.5
                                           7.0
                                           7.5
                                           8.0
                                           8.5
                                           9.0
                                           9.5
                                          10.0
                                          10.5
                                          11.0
                                          11.5
                                          12.0
                                          12.5
                                          13.0
                                          13.5
                                          14.0
                                          14.5
                                          15.0
                                          15.5
                                          16.0
                                          16.5
                                          17.0
                                          17.5
                                          18.0
                                          18.5
                                          19.0
                                          19.5
                                          20.0
                                          20.5
                                          21.0
                                          21.5
                                          22.0
                                          22.5
                                          23.0
                                          23.5
                                          24.0
                                          24.5
                                          25.0
                                          25.5
                                          26.0
                                          26.5
                                          27.0
                                          27.5
                                          28.0
                                          28.5
                                          29.0
                                          29.5
                                                             Wind Speed Bin (m/s)

                                                                Measured       Normal


                                          Frequency Distribution Comparison (Period 2)

                                   25.0
                 No Observations




                                   20.0
                                   15.0
                                   10.0
                                    5.0
                                    0.0
                                           0.0
                                           0.5
                                           1.0
                                           1.5
                                           2.0
                                           2.5
                                           3.0
                                           3.5
                                           4.0
                                           4.5
                                           5.0
                                           5.5
                                           6.0
                                           6.5
                                           7.0
                                           7.5
                                           8.0
                                           8.5
                                           9.0
                                           9.5
                                          10.0
                                          10.5
                                          11.0
                                          11.5
                                          12.0
                                          12.5
                                          13.0
                                          13.5
                                          14.0
                                          14.5
                                          15.0
                                          15.5
                                          16.0
                                          16.5
                                          17.0
                                          17.5
                                          18.0
                                          18.5
                                          19.0
                                          19.5
                                          20.0
                                          20.5
                                          21.0
                                          21.5
                                          22.0
                                          22.5
                                          23.0
                                          23.5
                                          24.0
                                          24.5
                                          25.0
                                          25.5
                                          26.0
                                          26.5
                                          27.0
                                          27.5
                                          28.0
                                          28.5
                                          29.0
                                          29.5
                                                             Wind Speed Bin (m/s)

                                                                Measured       Normal
                                                                                         19
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – FIXED MASTS


Failure to:
• examine site photos/visit site
• inspect site records
• choose site which reflects ‘regional’
   winds
• choose site with similar climatology
   to target site
• choose site with good long-term mean
• choose site with long enough
   concurrent period available?
• choose site with long enough historic
   period available?



Last requirement can create problems…




                                                                   20
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – FIXED MASTS




                                                                   21
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – FIXED MASTS




                                                                   22
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – FUTURE PROBLEM?



                                         However:
                                         • In US, ASOS masts recently re-
                                           equipped with sonic anemometers
                                         • In UK, UKMO has installed consistent
                                           instrumentation at all stations

                                         The problem:
                                         • instrument changes may destroy
                                           continuity
                                         • can result in limited number of
                                           reference sites being suitable
                                         • very sparse networks of low quality
                                           meteorological stations in many areas

                                         Outcome: often little or no suitable
                                           reference sites available!



                                                                                   23
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – FUTURE PROBLEM?



                                         However:
                                         • In US, ASOS masts recently re-
                                           equipped with sonic anemometers
                                         • In UK, UKMO has installed consistent
                                           instrumentation at all stations

                                         The problem:
                                         • instrument changes may destroy
                                           continuity
                                         • can result in limited number of
                                           reference sites being suitable
                                         • very sparse networks of low quality
                                           meteorological stations in many areas

                                         Outcome: often little or no suitable
                                           reference sites available!



                                                                                   24
2. REFERENCE SITE DATA: PITFALLS & COMMON PROBLEMS – MESO-SCALE DATA


As alternative, ‘virtual’ mast data may
be appropriate:
• NCEP/NCAR Re-Analysis 2 data (2.5
   deg resolution; 6 hour time base)
• Meso-scale data
• WorldWind Atlas
• Others…




Don’t forget that:
• must fulfill same requirements as
  fixed mast data!
• use with caution!
• last resort option!!



                                                                       25
3. MCP – PRE-PROCESSING 1



                            Get to know your data:
                            • create time series plots of target and
                              reference site data
                            • are time series in-phase?
                            • do time series display the same gross
                              trends?




                            In practice the process of identifying a
                            good reference site is iterative!


                                                                       26
3. MCP – PRE-PROCESSING 1



                            Get to know your data:
                            • create time series plots of target and
                              reference site data
                            • are time series in-phase?
                            • do time series display the same gross
                              trends?




                            In practice the process of identifying a
                            good reference site is iterative!


                                                                       27
3. MCP – PRE-PROCESSING 1



                            Get to know your data:
                            • create time series plots of target and
                              reference site data
                            • are time series in-phase?
                            • do time series display the same gross
                              trends?




                            In practice the process of identifying a
                            good reference site is iterative!


                                                                       28
3. MCP – PRE-PROCESSING 1



                            Get to know your data:
                            • create time series plots of target and
                              reference site data
                            • are time series in-phase?
                            • do time series display the same gross
                              trends?




                            In practice the process of identifying a
                            good reference site is iterative!


                                                                       29
3. MCP – PRE-PROCESSING 2


• Create scatter plots of target and reference site data
    – may give insight into choice of suitable MCP algorithm BUT...
    – scatter plots often misleading and need a 3rd dimension (example)
• generally need to ensure that correlation coefficient, r, is  0.7




                                                                          30
3. MCP – PRE-PROCESSING 2


• Create scatter plots of target and reference site data
    – may give insight into choice of suitable MCP algorithm BUT...
    – scatter plots often misleading and need a 3rd dimension (example)
• generally need to ensure that correlation coefficient, r, is  0.7




                                                                          31
3. MCP – CHOICE OF ALGORITHM - 1


Assuming we have in-phase, suitably pre-
averaged QC’d data, what MCP algorithm
should we choose?
• several classes of algorithm:
     – linear models (y = mx+c)
     – non-linear models
     – JPD-type models
     – neural network models
• within each class, several choices
  available
• all have strengths and weaknesses!


Choice might depend on what you want
to use MCP results for!


                                           32
3. MCP – CHOICE OF ALGORITHM - 1


Assuming we have in-phase, suitably pre-
averaged QC’d data, what MCP algorithm
should we choose?
• several classes of algorithm:
     – linear models (y = mx+c)
     – non-linear models
     – JPD-type models
     – neural network models
• within each class, several choices
  available
• all have strengths and weaknesses!


Choice might depend on what you want
to use MCP results for!


                                           33
3. MCP – CHOICE OF ALGORITHM - 1


Assuming we have in-phase, suitably pre-
averaged QC’d data, what MCP algorithm
should we choose?
• several classes of algorithm:
     – linear models (y = mx+c)
     – non-linear models
     – JPD-type models
     – neural network models
• within each class, several choices
  available
• all have strengths and weaknesses!


Choice might depend on what you want
to use MCP results for!


                                           34
3. MCP – CHOICE OF ALGORITHM - 1


Assuming we have in-phase, suitably pre-
averaged QC’d data, what MCP algorithm
should we choose?
• several classes of algorithm:
     – linear models (y = mx+c)
     – non-linear models
     – JPD-type models
     – neural network models
• within each class, several choices
  available
• all have strengths and weaknesses!


Choice might depend on what you want
to use MCP results for!
                                           From Paul van Lieshout’s ‘Wind resource analysis
                                           based on the properties of wind or “SKM Weibull’s
                                           correlation methodology evaluated”’ paper at All-   35
                                           Energy 2009 conference
3. MCP – CHOICE OF ALGORITHM – 2


• JPD methods, e.g. RES matrix method




                                        36
3. MCP – CHOICE OF ALGORITHM – 2


• JPD methods, e.g. RES matrix method




                                        37
3. MCP – CHOICE OF ALGORITHM – 2


• JPD methods, e.g. RES matrix method




                                        38
3. MCP – CHOICE OF ALGORITHM – 2


• JPD methods, e.g. RES matrix method




                                        39
3. MCP – DATA SUB-CATEGORISATION


                                   • typically data decomposed into sub-
                                     categories prior to applying MCP
                                   • typical sub-category is wind direction
                                   • 12 (or 16) directional sectors common
                                   • not always good choice - inspection of
                                     the wind rose may inform this
                                   • inspection of diurnal trend may help
                                     inform this choice (hourly)
                                        – if pronounced trend, consider a
                                          number of ‘time of day’ sectors
                                        – trying to capture periods with
                                          similar atmospheric stability
                                        – see Andy Oliver & Kris Zarling’s
                                          paper at AWEA 2009!
                                   Regardless of sub-categorisation, need
                                   enough data to populate all sectors
                                                                              40
 3. MCP – PREDICTION APPROACH & UNCERTAINTY ANALYSIS

                            Long Term Estimate (LTE)

                           Historic Estimate (HE)                    Site Measurements (AAE)

                                                                                                    Target Site

                                                                                                   Concurrent Period
                   Historic Reference Measurements                                                 Relationship (MCP)

                                                                                                    Reference Site
                                                                                      Time
Vn  VN  ({ M / N HE }  { M / n}   MCP   Inst )1 / 2 Vn'  VN  ({ M
              2               2          2       2                  '       2
                                                                                / N AAE }  { M / n}   %   Inst )1 / 2
                                                                                               2          2     2
 3. MCP – PREDICTION APPROACH & UNCERTAINTY ANALYSIS

                            Long Term Estimate (LTE)

                           Historic Estimate (HE)                    Site Measurements (AAE)

                                                                                                    Target Site

                                                                                                   Concurrent Period
                   Historic Reference Measurements                                                 Relationship (MCP)

                                                                                                    Reference Site
                                                                                      Time
Vn  VN  ({ M / N HE }  { M / n}   MCP   Inst )1 / 2 Vn'  VN  ({ M
              2               2          2       2                  '       2
                                                                                / N AAE }  { M / n}   %   Inst )1 / 2
                                                                                               2          2     2
 3. MCP – PREDICTION APPROACH & UNCERTAINTY ANALYSIS

                            Long Term Estimate (LTE)

                           Historic Estimate (HE)                    Site Measurements (AAE)

                                                                                                    Target Site

                                                                                                   Concurrent Period
                   Historic Reference Measurements                                                 Relationship (MCP)

                                                                                                    Reference Site
                                                                                      Time
Vn  VN  ({ M / N HE }  { M / n}   MCP   Inst )1 / 2 Vn'  VN  ({ M
              2               2          2       2                  '       2
                                                                                / N AAE }  { M / n}   %   Inst )1 / 2
                                                                                               2          2     2
3. MCP – SECOND STEP PREDICTIONS




                                   44
3. MCP – SECOND STEP PREDICTION VS GAP FILLING




                                                 45
3. MCP – CONCURRENT PERIOD & SEASONALITY

                                                                                        Com parison of Norm alised York HE for All Sites Investigated
                                                                         1.10                                                                              Sunflow er Electric
                                                                                                                                                           High Plains


 For most sites, the pattern of normal
                                                                                                                                                           Sleeping Bear
                                                                                                                                                           Somerset
                                                                         1.05

 seasonal variation means that the precise
                                                                                                                                                           Hopkins




                                              Normalised HE Wind Speed
 choice of concurrent period will affect                                 1.00


 the prediction
                                                                         0.95

 • to avoid use only full integer periods
   of data                                                               0.90       High variability initially, starting
                                                                                    to converge after 24 months
 • not always practical!                                                 0.85
                                                                                0           8760                       17520                       26280                   35040


 • if have less than a year of data, try to
                                                                                                                 Num ber of hours




   avoid extremes (e.g. summer/winter)
                                                                                        Com parison of Norm alised Matrix HE for All Sites Investigated
                                                                         1.10                                                                              Sunflow er Electric
                                                                                                                                                           High Plains
                                                                                                                                                           Sleeping Bear


 • can generally identify from data
                                                                                                                                                           Somerset
                                                                         1.05                                                                              Hopkins



   whether significant                        Normalised HE Wind Speed
                                                                         1.00



 • Probably more significant for sites
   with thermally, rather than pressure
                                                                         0.95




   driven, winds?                                                        0.90
                                                                                      In first year, long-term predictions
                                                                                      can be in error by ± 5 - 10 %!
                                                                         0.85
                                                                                0           8760                       17520                       26280                 35040
                                                                                                                 Num ber of hours




                                                                                                                                                                              46
3. MCP – CONCURRENT PERIOD & SEASONALITY

                                                                                            Com parison of Norm alised York HE for All Sites Investigated
                                                                         1.10                                                                                  Sunflow er Electric
                                                                                                                                                               High Plains


 For most sites, the pattern of normal
                                                                                                                                                               Sleeping Bear
                                                                                                                                                               Somerset
                                                                         1.05

 seasonal variation means that the precise
                                                                                                                                                               Hopkins

                                                                                    Seasonally-corrected




                                              Normalised HE Wind Speed
 choice of concurrent period will affect                                 1.00
                                                                                    estimate is more stable
 the prediction
                                                                         0.95       and shows less spatial
 • to avoid use only full integer periods
   of data                                                               0.90
                                                                                    variability
                                                                                      High variability initially, starting

 • not always practical!                                                 0.85
                                                                                      to converge after
                                                                                    Method shows 24 months
                                                                                                        promise!
                                                                                0               8760                       17520                       26280                   35040


 • if have less than a year of data, try to
                                                                                                                     Num ber of hours




   avoid extremes (e.g. summer/winter)
                                                                                            Com parison of Norm alised Matrix HE for All Sites Investigated
                                                                         1.10                                                                                  Sunflow er Electric
                                                                                                                                                               High Plains
                                                                                                                                                               Sleeping Bear


 • can generally identify from data
                                                                                                                                                               Somerset
                                                                         1.05                                                                                  Hopkins



   whether significant                        Normalised HE Wind Speed
                                                                         1.00



 • Probably more significant for sites
   with thermally, rather than pressure
                                                                         0.95




   driven, winds?                                                        0.90
                                                                                          In first year, long-term predictions
                                                                                          can be in error by ± 5 - 10 %!
                                                                         0.85
                                                                                0               8760                       17520                       26280                 35040
                                                                                                                     Num ber of hours




                                                                                                                                                                                  47
4. MCP – IDEAL PREDICTION STRAGEY


This might feature:
• a rigorous appreciation of
  errors/uncertainty is crucial!
• the use of ‘portfolio’ MCP predictions
• predictions based on multiple
  references sites, real and virtual
• consideration of whether predictions
  are consistent with expectations
• ‘Round Table’ discussions amongst
  colleagues
• some iteration is inevitable!




                                           48
CONCLUSIONS


• if approached with diligence and care, MCP can be a vital tool
• it requires attention to detail at every stage of the site assessment
  process, not just in MCP model building (tiny part overall!)
• you need to understand how to obtain:
    –   high quality (target) site data
    –   high quality, appropriately chosen, reference site data
• you need to understand the application and limitations of MCP software
• you need the skills, knowledge & experience to use it & interpret results
• experiments suggest that MCP success is far more to do with choice of
  high quality reference site than it is to MCP algorithm!




                                                                              49
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