ELECTRICITY LOAD FORECASTING BY ARTIFICIAL NEURAL NETWORK MODEL

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					INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING
 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME
                            & TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 1, January- February (2013), pp. 91-99
                                                                             IJEET
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2012): 3.2031 (Calculated by GISI)                 ©IAEME
www.jifactor.com




        ELECTRICITY LOAD FORECASTING BY ARTIFICIAL NEURAL
               NETWORK MODEL USING WEATHER DATA

                          Balwant singh Bisht1 and Rajesh M Holmukhe2
     1
       Post graduate student (M.E.Electrical Engineering), Electrical Engineering Department,
    Bharati Vidyapeeth Deemed University College of Engineering, Pune411043 (MS), India.
                                 Email: balwant_sb@rediffmail.com
         2
           Associate Professor in Electrical Engineering, Electrical Engineering Department,
    Bharati Vidyapeeth Deemed University, College of Engineering, Pune-411043(MS), India.
                               Email: rajeshmholmukhe@hotmail.com



   ABSTRACT

          This paper discusses significant role of advanced technique in short-term load
   forecasting (STLF), that is, the forecast of the power system load over a period ranging from
   one hour to one week. An adaptive neuro - wavelet time series forecast model is adopted to
   perform STLF. The model is composed of several neural networks (NN) whose data are
   processed using a wavelet technique. The data to be used in the model are both the
   temperature and electricity load historical data. The temperature variable is included because
   temperature has a close relationship with electricity load. The calculation of mean average
   percentage error for a specific region under study in India is done and results obtained using
   MATLAB’S ANN toolbox. This study proposes a STLF model with a high forecasting
   accuracy. In this study absolute mean error (AME) value calculated is 1.24% which
   represents a reasonable degree of accuracy.

   Key words: short term load forecasting, artificial neural network, power system

   1.     INTRODUCTION

          Short term load forecasting (STLF) studies began at early 1960’s. In 1971, a load
   forecasting system was developed by researchers in United States which used statistical
   approach. Subsequent to 1990’s researchers started to implement different approaches for
   STLF other than statistical approach mainly due to their requirement for huge data sets to

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME

implement STLF systems. In 2003 many STLF studies were carried using neural network
models.Load forecasting occupies a central role in the operation and planning of Electric
Power System. Load forecasting can be divided into three major categories: Long-term load
forecasting, Medium-term load forecasting and Short-term load forecasting. STLF precedes
many important roles carried in energy management systems (EMS), which continuosly
monitors the system and initiates the control actions in time critical Situations. STLF model
is critical important decision support tool for operating the electric power system securely
and economically.
      Load forecasting can be made by different methods like regression analysis, statistical
methods, artificial neural networks, genetic algorithm, fuzzy logic, etc.In the recent years,
many researchers have tried to use the modern techniques based on artificial intelligence. Of
all techniques, the artificial neural network (ANN) receives the most attention. ANN is
regarded as an effective approach and is now commonly used for electricity load forecast.
The reason for its popularity is its ease of use and its ability to learn complex input-output
relationship. The ability to learn gives ANN a better performance in capturing nonlinearities
for a time series signal. Therefore, the study in this paper proposes a model comprising
neural networks as its forecasting tool. This paper explores an adaptive neuro-wavelet
model for Short Term Electricity Load Forecast (STLF). Both historical load and temperature
data, which have important impacts on load level, are used in forecasting by the proposed
model. To enhance the forecasting accuracy by neural networks, the non-decimated Wavelet
Transform (NWT) is introduced to pre-process these data.

    The objective of this study is conduct out short-term load forecasting using MATLAB’S
ANN Toolboxes. Artificial Neural Network (ANN) Method is applied to forecast the short-
term load for a large power system in one state of India. A nonlinear load model is proposed
and several structures of ANN for short term forecasting are tested.

   The data used in the model, both the weather and electricity load historical data were
obtained from metrological office of Government of India (GOI), Pune(India) and state load
dispatch center,Mumbai(India).Field visits to state load dispatchcenter,Mumbai(India) were
done.

2.     LITERATURE SURVEY

        One of the first published studies was done by Heinemann et al. in 1966 which dealt
with the relationship between temperature and load. Lijesen and Rosing (1971) developed
load orecastig systemwhich used statistical approach. In this study, estimated average root
mean square error value was 2.1%. Hagan and Behr (1987) forecasted load using a time
series model. With this model, the nonlinear relationship between load and temperature data
during winter months was clearly observed. Park et al (1991) claimed that the statistical
methods like regression and interpolation did not provide reliable prediction performances
that of artificial neural network (ANN). The average absolute errors of the one-hour and 24-
hour ahead forecasts were calculated as 1.40% and 2.06%, respectively. This method was
found successful when compared with the regression method for 24 hour ahead forecasts with
an error of 4.22%. Xu(2003) considered market forecasting by using various new techniques,
such as wavelet, neural network and support vector machine.The author explored how
different models for electricity Load and price forecast have been developed, which are

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME

able to forecast at one or more time steps ahead. Dong et al (2003) presented an adaptive
neuro-wavelet model for Short Term Electricity Load Forecast (STLF). Both historical
load and temperature data, which have important impacts on load level, are used in
forecasting by the proposed model. To enhance the forecasting accuracy by neural
networks, the Non-decimated Wavelet Transform (NWT) is introduced to pre-process
these data. Benaouda et al (2005) looked at wavelet multiscale decomposition based
autoregressive approach for the prediction of one-hour ahead load based on historical
electricity load data.Ching-Lai Hor et al (2005) made use of forecasting model including
both climate-related and socioeconomic factors that can be used very simply by utility
planners to assess long-term monthly electricity patterns using long-term estimates of
climate parameters, gross domestic product (GDP), and population growt. Myint et al
(2008) proposed a novel model for short term loadforecast (STLF) in the electricity
market.In this study the prior electricity demand data are treated as time series. The model
is composed of several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program written with
MATLAB.

3. KEY FEATURES OF ARTIFICIAL NEURAL NETWORK BASED SHORT
TERM LOAD FORECASTING (ANNSTLF)

     Advantages of ANNSTLF

1. Adaptive learning: An ability to learn how to do tasks based on the data given for
training or initial experience.
2. Self-Organization: An ANN can create its own organization or representation of the
information it receives during learning time.
3. Real Time Operation: ANN computations may be carried out in parallel, and special
hardware devices are being designed and manufactured which take advantage of this
capability.

     Limitations in the ANNSTLF

Several difficulties exist in short-term load forecasting such as precise hypothesis of the
input-output relationship, generalization of experts’ experience, the forecasting of
anomalous days, inaccurate or incomplete forecasted weather data.

4.       MATHEMATICAL MODEL OF A NEURON

       A neuron is an information processing unit that is fundamental to the operation of
a neural network. The three basic elements of the neuron model are. A set of weights, an
adder for summing the input signals and activation function for limiting the amplitude of
the output of a neuron.




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                 Figure 1 Model of an artificial neural network (ANN)

5.    NEURAL NETWORK (NN) MODEL WITH WAVELET ENHANCEMENT
FOR TIME SERIES FORECAST

       To improve the quality of the raw input signal for time series forecast, the
neural network model is enhanced with multi-scale wavelet transform. Figure below
shows an illustration of the wavelet enhanced neural network model for time series
forecast.The inputs given are: Hourly load demand for the full day, day of the week,
min/max/ average daily temperature and min/max daily humidity.




                  Figure 2 Input-output schematic for load forecasting

6.      MEAN AVERAGE PERCENTAGE ERROR (MAPE)



 ; PA = Actual load demand, PF = Forecasted load demand, N= Number of time sections.



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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME

Following table shows the different methods of the electric load forecasting and mean
absolute percentage error based on literature survey. The figure shows the neural network
method is more accurate than any other methods. Therefore method used in this study i.e
artificial neural network based load forecasting method is justified.

                                         Table 1 Various STLF methods & MAPE
                                                   Forecasting                 MAPE
                                                   Methods
                                                   Regression                  3.74%
                                                   Time Series                 3.13%
                                                   Expert System               2.74%
                                                   Fuzzy Logic                 2-3%
                                                   Super Vector                2.14%
                                                   Machine
                                                   Neural Network              1.81%

Figure 3 Actual v/s forecasted load for Sunday


7. RESULTS

       The results obtained from testing the trained neural network on new data for 24 hours
of a day over a one-week period are presented below in graphical form. Each graph shows a
plot of both the predicted and actual electrical load in MW values against the hour of the
day. The absolute mean error % (AME %) between the predicted and actual loads for each
day has been calculated and presented in the table. Overall, these error values translate to an
absolute mean error of 1.24% for the network. This represents a high degree of accuracy in
the ability of neural networks to forecast electric load.




          Figure 4 Neural network training              Figure 5 Mean squared error
             at first little iteration.                     at first little iteration




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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME




          Figure 6 Neural network training after            Figure 7 Mean squared error after 10000
                    10000 iterations                                    iteration




                          Figure 8 Comparison between actual targets and
                                          predictions



                          Table 2 Mean average percentage error
                                Day         Marc        Augu     Januar
                                            h 3rd       st 2nd     y 1st
                                            week        week      week
                            Sunday          0.74        2.28     1.47
                            Monday          0.69        0.72     1.09
                            Tuesday         0.18        0.23     2.38
                            Wednesday       1.41        1.81     0.21
                            Thursday        1.50        1.07     0.62
                            Friday          0.62        1.27     3.08
                            Saturday        3.24        0.94     0.39
                            Average         1.14        1.18     1.32



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     8. Characteristics of the power system load
Various factors that influence the system load behavior, can be classified into the major
categories as weather, time, economy, random disturbance etc




       Figure 9 Hour load profile of grid in this study for a week of March 2010


From the above daigram it is seen that ,typically load is low and stable from 0:00 to 6:00; it
rises from around 6:00 to 9:00 and then becomes flat again until around 12:00; then it
descends gradually until 17:00; thereafter it rises again until 19:00; it descends again until the
end of the day.

CONCLUSION

        The result of adaptive neuro-wavelet time series forecast model used for one day
ahead short term load forecast for the considered area under study in India has a good
performance and reasonable prediction accuracy. Its forecasting reliabilities were evaluated
by computing the mean absolute error between the exact and predicted electrcity load
values.We were able to obtain an Absolute Mean Error (AME) of 1.24% which represents a
high degree of accuracy. The results suggest that ANN model with the developed structure
can perform good prediction with least error and finally this neural network could be an
important tool for short term load forecasting. The accuracy of the electricity load forecast is
crucial in better power system planing and reliability.

ACKNOWELDGEMENT

       Bharati Vidyapeeth Deemed University College of Engineering, Pune (India) for
providing MATLAB software and all necessary lab & library facilities.Metrological
department of Government of India for weather data. State load dispatch center, Mumbai
(India) for load data.



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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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REFERENCES

[1] H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: literature survey and
         classification,” International Journal of Systems Science, vol. 33, no. 1, 2002.
[2] D. Benaouda, F. Murtagh, J. L. Starck, and O. Renaud, “Wavelet-Based Nonlinear Multiscale
     Decomposition Model for Electricity Load Forecasting,” Sciences-New York, no. 1, pp. 1-47,
     2005.
[3] Ly Fie Sugianto Xue-Bing Lu School of Business Systems. “Demand forecasting in the
     deregulated market: a bibliography survey”
[4] M. Buhari and S. S. Adamu, “Short-Term Load Forecasting Using Artificial Neural Network,”
     Computer, vol. I, 2012.
[5] Z. Y. Dong, X. Li, Z. Xu, K. L. Teo, and S. Lucia, “weather depenent electricity market
     forecasting with neural networks , wavelet and data mining techniques” ,School of Information
     Technology and Electrical Engineering,1998.
[6] M. T. Hagan and S. M. Behr, “3, August 1987,” Power, vol. 0, no. 3, pp. 785-791, 1987.
[7] C.-lai Hor, S. J. Watson, and S. Majithia, “Analyzing the Impact of Weather Variables on
     Monthly Electricity Demand,” IEEE transactions on power systems, vol. 20, no. 4, pp. 2078-
     2085, 2005.
[8] F. Mosalman, A. Mosalman, H. M. Yazdi, and M. M. Yazdi, “One day-ahead load forecasting by
     artificial neural network,” Power, vol. 6, no. 13, pp. 2795-2799, 2011.
[9] P. Murto,“Neural network models for short-term load forecasting”, Department of Engineering
     Physics and Mathematics Pauli Murto,” 1998.
[10] D. C. Park, R. J. Marks, L. E. Atlas, and M. J. Damborg, “Electric load forecasting using an
     artificial neural network - Power Systems, IEEE Transactions on,” Power, vol. 6, no. 2, pp. 442-
     449, 1991.
[11] M. Park, “Adaptive forecasting,” Power, pp. 1757-1767.
[12] L. Wang, “Short-term Electricity Load Forecasting Based on Particle Swarm Algorithm and
     SVM,” no. Vc.
[13] C. Xia, J. Wang, and K. Mcmenemy, “Electrical Power and Energy Systems Short , medium and
     long term load forecasting model and virtual load forecaster based on radial basis function neural
     networks,” International Journal of Electrical Power and Energy Systems, vol. 32, no. 7, pp.
     743-750, 2010.
[14] Z. Xu, Z. Y. Dong, W. Q. Liu, and S. Lucia,“Neural network models for electricity,” 1987.
[15] M. M. Yi, K. S. Linn, and M. Kyaw,“Implementation of Neural Network Based Electricity Load
     Forecasting,” Engineering and Technology, pp. 381-386, 2008.
[16] G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks : The state
     of the art,” International Journal of Forecasting, vol. 14, pp. 35-62, 1998.
[17] E. Banda K. A. Folly, Short Term Load Forecasting Using Artificial Neural Network, IEEE,
     POER Tech, 2007
[18] Z. Xu , Z. Y. Dong , W. Q. Liu,Neural Network Models For Electricity Market Forecasting
[19] Z.Y. Dong X. Li Z. Xu K. L. Teo, Weather Dependent Electricity Market Forecasting with
     Neural Networks, wavelet and Data Mining Techniques
[20] A.padmaja, V.S.vakula, T.Padmavathi and S.V.Padmavathi, “Small Signal Stability Analysis
     Using Fuzzy Controller And Artificial Neural Network Stabilizer” International Journal of
     Electrical Engineering & Technology (IJEET), Volume 1, Issue 1, 2010, pp. 47 - 70, Published
     by IAEME
[21] Soumyadip Jana, Sudipta Nath and Aritra Dasgupta, “Transmission Line Fault Classification
     Based On Wavelet Entropy And Neural Network” International Journal of Electrical Engineering
     & Technology (IJEET), Volume 3, Issue 2, 2012, pp. 94 - 102, Published by IAEME




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6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME

Websites
  1. http://mahasldc.in/ Accessed in year 2012
  2. http://www.getcogujarat.com/ Accessed in year 2012
  3. http://www.imdpune.gov.in/ Accessed in year 2010
  4. http://www.kalkitech.com/ Accessed in year 2011
  5. http://www.mathworks.com /Accessed in year 2011
  6. http://www.sldcguj.com / Accessed in year 2012
  7. http://www.wunderground.com/ Accessed in year 2010
  8. www.mahasldc.in / Accessed in year 2012

Field Visits
    1. Metrological department,Pune(India)
    2. State load dispatch center,Mumbai(India)
    3. State Electrcity Board office,Pune(India)

Acronymns
    1. AME :Absolute mean error
    2. ANN:Artificial neural network
    3. ANNSTLF: Artificial neural network based Short term load forecasting
    4. EMS : Energy management systems
    5. GDP : Gross domestic product
    6. GOI : Government of India
    7. MAPE: Mean average percentage error
    8. NN : Neural Networks
    9. NWT: Non-decimated Wavelet Transform
    10. STLF : Short-term load forecasting




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