Short Term Load Forecasting with Artificial Neural Network via

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					 Short Term Load Forecasting with Artificial Neural Network via case study

         1R.M.Holmukhe2Mrs.Sunita Dhurnale, 3Mr.P.S. Chaudhari, 4Mr.P.P.Kulkarni
                                 "Asslstant Professor,
                         1.2ElectricalEngineering Department,
                        Bharati Vidyapeeth Deemed University,
                                College of Engineering,
                                       Pune, India
                               3.4Scientist,DRDO, Pune

  Abstract-In the decade 2000-2010, Indian        •    Enforcement of rationalized tariff and
Electricity sector has acknowledged the           ABT.
major changes in statutory framework,             •    Major grants (more than 500 Billions
operational framework, and policies of            INR) under 11th plan under R-APDRP.
Governments.       Main reasons       of such
                                                  •    Introduction of competition and Open
developments and alterations could be
                                                  •    Formation      of   Indian    Energy
•     Worldwide need of fuel or natural           Exchange.
energy resources saving.
•      Meeting regular energy requirement                    I.     INTRODUCTION
of the India.                                      The paper emphasizes the requirement,
•     Eliminating monopoly of the SEBs.            types and development of load forecasting
•     Rationalization of Tariff, introduction      applications tools to meet routine regulatory
of power market I open access to render            and operational requirements I compliances
benefits to         consumers.                     by Transmission Supply Users (TSUs) of
•     Increasing    level of Standard        of    the changed regime of Indian Power Sector
Performance,       qualityof     supply    and     such      as     Generation       Companies,
services.                                          Transmission     Companies,      Independent
•     Reduction     in    Transmission     and     Power Plants, Distribution Companies,
Distribution losses.                               Trading Companies, Open Access Users
•     Contribution     to    reducing   global     and consultants.
warming and support to global CDM.
                                                           II.     OBJECTIVE SCOPE
Major developments of this decade are                ~   To highlight on the load forecasting
listed below.                                          needs and importance to operational
•     Enactment of Electricity Act 2003,               mechanisms under changed regime of
Finalization of National Electricity Policy            power sector in India.
and National                                         ~ To reach detailed enlightenment on
      Tariff Policy.                                   available models I tools Itechniques
                                                       Imethodologies of load forecasting.
•     Restructuring    of     SEBs     and
unbundling of DISCOMs.                               ~ Understanding practical adoption of
                                                       load forecasting tool through case
•     Formation of SERCs, CERC, ATE and
                                                       study presentation of one of the Utility
making them operational.
                                                       in Maharashtra.
            Paper is organized in main sections.             Jon these schedules Generators prepare
             ••• Need of load forecasting.                   generation schedules. SLDC controls and
             ••• Load       forecasting     types,    load   monitors these operations.ABT mechanism
                 forecasting tools and methodologies         enforces incentive and disincentives to
                 in details.                                 Generators    and    TSUs      as    per  the
             ••• Case        study      and     concluding   compliances and non-compliances to the
                 annotations.                                submitted schedules. UI calculations and
                                                             balance and settlement is done on the ABT
             III. NEED OF LOAD FORECASTING                   compliant meter data and tariff determined by
              AND LOAD FORECASTING TYPES                     ERCs.

           a) Need of Load Forecasting:                      b) Types of Load Forecasting:
          1. Planning:                                       Load forecasting types are worked out as per
         Every participant viz GENCO, TRANSCO,               the operational requirements.
         DISCO and Traders need load forecast                1.    Long term load forecasting    (LTLF):
         inputs to prepare new schemes of extension          Applicable for system and long term network
         or enhancements or capacity additions or            planning.
         infrastructure development. The network and         2. Mid term Load Forecasting (MTLF):
         system planning is always based on load             Applicable for quarterly, half yearly and
         requirements. Advanced        load forecasting      yearly                                    LF
         tools or applications gives appropriate future      needs.
         long term load requirements.                        3.   Short term Load Forecasting (STLF):
         A major component of the ARR is investment          Applicable for day ahead and week ahead LF
         CAPEX in Infrastructure Projects for network        needs.
          enhancement and restructuring for loss
          reduction. Load forecasting      results give           IV. LOAD FORECASTING TOOLS &
         accurate inputs for optimal CAPEX proposals                      METHODOLOGY:
         and competitive tariff to consumers.
         2. Estimating sales forecast for ARR.               1.   Artificial Neural network.(ANN)
         Under new statutory regime, DISCOMS have            2.   Fuzzy logic (FL).
         to furnish ARR         to ERCs       for Tariff     3.   Autoregressive model.
         Determination. Tariff is determined on the          4.   Similar day approach.
         basis of sales forecast. Accurate realization       5.   Time series.
1"7'"0   or recovery as per recovery is always hinged        6.   Expert system.
         to accurate sales forecasting sale forecasting      7.   Support vector machine.
         is estimated on the basis of load forecasting
                                                             ANN and FL are the popular and commonly
                                                             used mathematical tools for LF applications.
         3. Furnishing Day ahead Schedule to
         LDCs by DISCOMs.                                    Detailed explanations and calculations are
                                                             placed below for ANN LF models.
         GENCOs get fixed charges for its asset
         implementation and maintenance in the ARR.          4.1 Artificial Neural Network.(ANN):
         However, they get recurring and FCAs                Introduction:
         through monthly       energy    charges   from      Short term load forecasting is an essential
         DISCOMs.                                            tool in operation and planning of the power
         Apart from above DISCOMs and other TSUs             system.       It helps  in coordinating the
         are governed by ABT mechanism. Under                generation and area interchange to meet the
         ABT, DISCOMs and receiving TSUs have to             load demand. It also helps in security
         submit day ahead schedule to LDCs. Based \          assessment, dynamic state estimation, load
  management and other related functions. In          '.----------------:~.
                                                          FIGURE 1
  the last few decades, various methods for                   SINGLE PROCESSING UNIT (PE)
  short term load forecasting    have been
  proposed. The methods vary from simple                                         NEURON
  regression and extrapolation    of fading
  memory Kalman filter and knowledge based
        Among the various methods available in                     .
  the literature, most can be classified into two
  categories. In the first category are the
                                                            Y •.
  methods, which rely solely on the past data
  and fit the load pattern as a time series. In
  the second category are the methods, which          4.1.1     ARTIFICIAL          NEURAL      NETWORK
  give emphasis to the weather variables, i.e.,       (ANN):
  temperature, humidity, light intensity, etc, and            Artificial        Neural      Networks     are
  find a functional relationship between these           increasingly      finding     use as alternative
  variables and the load demand.                        computational paradigm for solving complex
                                                        problems        like pattern       recognition  etc.
          Recently, Artificial  Neural Networks          Neurons in ANN can be viewed as simple
    (ANN) has been used for short term load             processing elements (PE). A commonly used
    forecasting. Both time series models and            PE representation of an artificial neuron is
   weather dependent models have been used              shown in Fig 1. The PEs can be
    in ANN based short tem load forecasting. In         interconnected           in   various    topologies.
    this paper, a short-term load forecasting           Depending         on the various topologies,
    method using the ANN is proposed.             A     activation functions and weight change
    multilayered feed forward (MLFF) neural             strategies,       a large       number     of ANN
    network with back propagation          learning     architectures have been developed, e.g.,
    algorithm has been used because of its              Back propagation, Hotfield net, Kohonen net,
   simplicity and good generalization property.         etc. Among the various ANN architectures
   The input, to the neural network is based only       available in the literature, the multilayer feed
   on past load data and are heuristically             forward (MLFF) network with error back
   chosen in such a manner that they inherently         propagation learning algorithm has been
   reflect all the major components, such as,          selected for this problem mainly because (i) it
   trend, type of day, load shape as well as            is the most simple and comprehensive neural
   weather which influence the system load.            approval for model based prediction and/or
         The main contributions of this paper are:     control and          (ii) it has the generalisation
(i) Heuristic choice of a small set of input which     capability.
inherently represents the major components of
the load pattern (ii) introduction of a stopping       4.1.2 Multilayered Feed Forward Network
criteria during learning phase to avoid over           (MLFF):
fitting of the network to learning examples, and       In MLFF network the PEs are arranged in
(iii) A detailed analysis of the generalisation        layers and only in adjacent layers are
properties like interpolation/extrapolation ability    connected. It has a minimum of layers of
of the ANN, working life of a trained network,         PEs; (i) the input layer, (ii) the middle or
ie, useful period of a network after which a           hidden layer(s), (iii) the output layer. The
retaining is required etc                              information propagation is only in the forward
                                                       direction and there are no feedback loops. A
                                                       MLFF network topology is shown in Fig 2. \
 In order to obtain bounded output from PEs a                                                           8j   = OJ(~- OJ)L8kWkj
 sigmoidal activation function is chosen where
 output is limited to (0, 1) for the input range (-                         where k is over all PE's in the layer above the
 oc,oc).                                                                    jth layer of PE and 11,the learning rate, oc, the
       The MLFF network uses separate                                       momentum factor. The momentum term
 stages for learning and operation. The                                     helps in faster convergence of the algorithm.
 learning problem can be stated as: given a                                 Once the network gets trained, the resulting
 set of input-output pairs (11 • 01), ..... (In . On),                      connection weights are frozen. In the
 find the interconnection weights Wjj for each                              operation stage the network is used to
 interconnection of ANN such that the network                               compute an output from a set of inputs.
 maps Ij to OJfor i 1, 2, 3,     =   , n, as closely
                                                                           4.1.3 PROPOSED METHOD
 as possible.
                                                                           Characteristics of the Load Data
 The     error     back    propagation    learning                                 In order to reflect the load behavior in
 algorithm, the interconnection weights are                                  the input information, the historical hourly
 adjusted such that the error function      E                    = -         load data for 1 year of a number of systems
 (1/2) L k (tk - Ok) is minimized, where,                                    were analyzed. It was observed that the load
 tk desired output for unit in layer k                                       data exhibits a daily and weekly periodicity. It
 Ok actual output for unit in layer k                                        was also observed that the daily load pattern
                                                                             for the working days showed marked
             FIGURE 2
                                                                             similarity whereas the holiday load patterns
             FEED l'"ORW ARD (MLFF) NETWORK
                                                                             were quite different from those of the working
                        OUTPUT   PATT'ERN
                                                                             days. Therefore, hourly loads for working
                                                                             days and holidays were treated separately.
                                                                             Auto-correlation of hurly load was obtained

             I                                                                                                    -
                                                                                                 L n-k(Yt- y-)(Yt+k y-)
                                                                                                                                t   =1
                                                                                                             rk = ---------------------------
                                                                                                                  L n (Yt_ y-)2
                                                                                                                       t 1          =
                                                                                rk = auto-correlation factor for time lag k

                                     Figure 2
                        INPUT PATTERN                                         I
                                                                              I      .,.(1)                                                          ··1-

The minimisation process is based on gradient                                 .
                                                                                                                       V            VVVVVV
descent algorithm. The interconnecting weights
between jth layer (upper layer) neurons and ith
                                                                                    -I.O()    --J:-.-.-".......~~~..........,....                -:r:rr""........,..,.....,..!
                                                                                                                                          . ......",
layer (lower layer) neurons is modified using                                                                       H(),-,t,.           •.•.._

the following relationship.
                                                                                    Figure 3Auto-Correlation Factor (rk) for
                    Wjj (new)           =       Wjj (old) + 118jOJ+   o;
                                                                                        two weeks load on best system
 [~Wjj (old)]
 Where, if, PEj is an output layer PE, then
 8j OJ(tj - OJ)(1 - OJ)
 if, PEj is an hidden layer PE, then
         n total number of available data           ie, input layer, one hidden layer and output
         y- = mean value of that available data     layer. The number of neurons in the input
         Yh hth hour data                           layer is equal to the number of variables in
 for two weeks (336 hours) data for the test        the input data. The output layer consists of
 systems and is shown in Fig 3. Loads for 24        one neuron. Although, the choice of number
 hours and 168 hours are highly correlated          of hidden layer neurons is arbitrary and a
 and based on these observations. Five hourly       optimal number of hidden layer neurons is
 loads were heuristically chosen and used as        generally obtained through trail and error. On
 input information. These inputs are as             the basis of a large number of simulations a
 follows: (i) previous hour load (L1), (ii)         large number of neurons in the hidden layer
 previous       to    previous    hour     10ad(L   leads to large training time, as well as, it
 2),(iii)previous day(same day type) same hour      creates a grandmother network. The new
 load(L24),                                         network memorizes the learning patterns very
                                                    well but does not perform well for new set of
 (iv) previous week same day and same hour          input. Whereas, with too small number of
 load(L168), (v) previous week same day but         hidden layer neurons, the network has
 previous hour load(L169).                          difficulty in learning, as it is unable to create
                                                    the required complex decision boundaries.
                                                    Therefore, a good starting point for optimal
 Among these, L24 and L168 reflect the daily        choice of hidden layer neuron by trail and
 and weekly periodicity of the hourly load. L1-     error is to use geometric mean of the input
  , L2, L168 and L169 reflect the trend of the      and output layer neurons.
 hourly load pattern and L1 and L2 also             Stopping Criteria:
  implicitly reflect the weather effect.
                                                           Fig.   4 shows         the    convergence
  Scaling of the Input and Output Data
                                                    characterizes of the learning algorithm for
  \     The input and output variables for the
                                                    IEEE 24 bus system. The testing was done
 neural network will have very different ranges
                                                    after every iteration during learning. Initially,
 if the actual hourly load data is directly used.
                                                    the Mean Square error (MSE) for both the
 This may cause convergence problem during
                                                    training and testing set decreases gradually.
 the learning process. To avoid this, the input
                                                    But after some iterations, is, around 2000
 and output load data were scaled such that
                                                    iterations, the MSE for the testing examples
 they were within the range (0,1), with majority
                                                    increases, through, the MSE for the learning
 of the data having values near to 0.5. For this
                                                    examples still decreases, is, network starts
 purpose the actual load was scaled using the
                                                    over fitting for the training set from this point.
 following relationship.
                                                    Thus, the learning should be stopped at this
                  L - Lmin                          point.
               Ls   = ------------------------      Simulation   and Results
                      Lmax - Lmin
 Where,L  =    the actual load                      Test Systems:
       L,=    the scaled load which is used as
 input to the net                                         The developed algorithm was tested
       Lmax=    the maximum load, 1.5 to 2 times    with hourly load data for the following
 the peak load for the whole year                   systems: (i) OSEB (Orissa State Electricity
       Lmin = the minimum load, 0.5 to 0.75         Board, India); (ii) IEEE 24 bus reliability
 times the valley                                   system. The two systems have quite different
ANN Architecture:                                   daily load patterns. The load data of OSEB
 The artificial neural network architecture used    system for the year 1990 has peak load in
 is a feed forward network with three layers,       August and a valley load in March. While the
  IEEE 24 bus reliability test system is a winter                                                                                                      In order to test the generalization
  peaking system with peak load in December,                                                                                                   property or exploitation        and interpolation
  it has a second peak in June at 90% of                                                                                                       capability of the net in more details, the hourly
  annual peak.                                                                                                                                 load data was divided into four groups, ie, A, B,
                                                                                                                                               C and D. Four distinct training and testing sets
  As daily load pattern for normal working days
                                                                                                                                               were prepared as detailed in Table 3. The
  were quite different from those of weekend
                                                                                                                                               results are presented in Table 3. From Table 4
  days and holidays. The load data for each
                                                                                                                                               it can be seen that the network is able to
  system was divided into two groups, ie,
                                                                                                                                               perform both interpolation and extrapolation
  normal working days and weekend days and
                                                                                                                                               quite well with less than 5% average error.
  holidays. These two sets were treated
                                                                                                                                               Extrapolation ability is of particular interest as it
                                                                                                                                               shows that network can predict even for
                                                                                                                                               unknown situations. Only for few stray cases,
                                                                                                                                               the errors have been more than 5%.
                                                                                                                                                                  V. CASE STUDY

                                                                                                                                                   Development and Implementation of LF
                                                                                                                                                   application for one of the distribution utilities
                            .•........                                                                                                             at Mumbai.
                                                    .•..... .•...•..•.. '"""' •..•............•
                                                           ;.                                     -••..........•...•~ ...•...•
                                                                                                                       .;        "                 Requirement:
        2.0                                                                                                                                        Short term load forecasting reports for
        e , fS    """~o~oW"'1'"'t''''~b'o<r'--'-'-''                                                       ~otQd •• -;000
                                                                                                                                                   submitting day ahead schedule at 15 minutes
                                    NO.: of                 H~,.Qtfon                        ~---
                                                                                                                                                   interval   to   SLDC,     Kalwa      for   ABT
                                    l"ll",,"      .'~\      .".~.      'IJI<:H_.                           _

        t   t.'                         .....!t~!.J
                                            r                                                           .• ~!'I~-:'!.U,~,.r~IN;-
                  '                                                                                                                  ,             compliances.

    Figure4Convergence  characteristics                                                                                                  of   @, Data required for application development:
            MLFF Neural Network                                                                                                                    1. Three years past load data at 15
Supervised            Learning                                                                                                                     minutes interval.
                                                                                                                                                   2.    Three years congruent weather data of
      The ANNs used for forecasting hourly                                                                                                         humidity, rainfall, temperature and wind
 load consists of five input neurons, two 25                                                                                                       direction.
                                                                                                                                                   3.    City holiday calendar.
 25hidden layer neurons and one output layer
                                                                                                                                              @;    Data required for running load forecast:
 neuron. Twenty-four separate ANNs one for
                                                                                                                                                   1. Weather forecast at 15 minutes interval.
 each hour forecast, were trained using the                                                                                                        2.   Actual weather of past day at 15
 input-output data pairs. Separate ANNs were                                                                                                       minutes interval.
 trained for weekdays and weekends. Thus, a                                                                                                        3.   Major network and load changes.
 total of 48 ANNs were trained for each
 system. On the basis of a large number of
 simultaneous optimal values for the learning
 coefficientm) and momentum factor(a) used
 for training of each ANN was obtained. After
 the convergence of the training algorithm,
 each ANN was tested using input output pairs
 from the test set data.
Testing Generalization                                              Property
DESCRIPTION        FOR TRAINING AND TESTING                         TABLE 4
TEST                                                            RESULTS FROM GENERALISED
Training               Set Data          Testing Set Data
Case I               Taken randomly        Taken randomly       Training Error (%) Testing Error (%)
                       from region S&C     from region A&D
extrapolating   ability in
                                                                                Max.       mm        avo           max
                                                                min       avo
Case II              Taken randomly        Taken randomly       Case I      - 3.62      0.213      1.487     - 5.334
                                                                0.22      2.26
                       from region C&D      from region A&S
extrapolating   ability in
                                                                Case II   - 2.93         0.085     0.986      - 6.269
upward direction
                                                                0.02    3.89
Case III              Taken randomly        Taken randomly
Testing                                                         Case III    - 3.19      - 0.040    1.40            4.915
                         from region A&S     from region C&D
extrapolating   ability in                                      0.12     3.36

downward direction                                                                                 1.26
                                                                Case IV   - 3.91         0.048                - 4.594
Case IV              Taken randomly            Taken randomly   -0.01   0.97
                       from region A,S,       from region A,S

                                                                   @;    SCREEN SHOTS    of the LF applications.
                                                                                                                                             2.   G.Gross     and   F.D.Galiana.    'Short-term Load   Forecasting',
                                                                                                                                                  Proceedings of IEEE, vol 75, nol2, December 1987, p 1558-1573.
                                                                                                                                             3.   AK    Mahalnobis, D.P.Kothari and S.I.Ahson. 'Computer Aided
                                                                                                                                                  Power System Analysis and Control', Tata Mcgraw Hill Publication
                                                                                                                                                  Co, New Delhi. 1988.
                                                                                                                                             4.   Paper     on    LF    by     EugeneAR?inberg   State   University
                                                                                                                                                  ofNewYork,Stony   Brook

                                                                                                                                             I.     COM: Clean Development Mechanism.
                                                                                                                                             2.      SEB: State Electricity Board
                                                                                                                                             3.      SERC: State Electricity Regulatory Commission.
                                                                                                                                             4.     CERC: Central Electricity Regulatory Commission.
                                                                                                                                             5.     CEA: Central Electricity Authority.
                                                                                                                                             6.      DISCOM: Distribution Company.
                                                                                                                                             7.     TRANSCO: Transmission Company.
                                                                                                                                             8.     GENCOs: Generation Companies.
                                                                                                                                             9.      STU: State Transmission Utility.
                                                                                                                                             10.    ABT: Availability Based Tariff.
                          ~-Y-v""Y- "'~j.-'C::::
                          •                                                                                                                  II.    R-APDRP: Restructured Accelerated Power Development         and
                                                                                                                                             Reforms Programme.


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