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Electricity load forecasting by artificial neural networkmodel

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Electricity load forecasting by artificial neural network modelDocument Transcript

1. 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.aspJournal Impact Factor (2012): 3.2031 (Calculated by GISI)

©IAEMEwww.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 91

2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print),

ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEimplement STLF

systems. In 2003 many STLF studies were carried using neural networkmodels.Load forecasting

occupies a central role in the operation and planning of ElectricPower System. Load forecasting can be

divided into three major categories: Long-term loadforecasting, Medium-term load forecasting and Short-

term load forecasting. STLF precedesmany important roles carried in energy management systems

(EMS), which continuoslymonitors the system and initiates the control actions in time critical Situations.

STLF modelis critical important decision support tool for operating the electric power system

securelyand economically. Load forecasting can be made by different methods like regression analysis,

statisticalmethods, 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. Ofall techniques, the

artificial neural network (ANN) receives the most attention. ANN isregarded 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-outputrelationship. The ability to learn gives ANN a better performance in

capturing nonlinearitiesfor a time series signal. Therefore, the study in this paper proposes a model

comprisingneural networks as its forecasting tool. This paper explores an adaptive neuro-waveletmodel

for Short Term Electricity Load Forecast (STLF). Both historical load and temperaturedata, which have

important impacts on load level, are used in forecasting by the proposedmodel. To enhance the

forecasting accuracy by neural networks, the non-decimated WaveletTransform (NWT) is introduced to

pre-process these data. The objective of this study is conduct out short-term load forecasting using

priyanka4456

MATLAB’SANN 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 proposedand several

structures of ANN for short term forecasting are tested. The data used in the model, both the weather

and electricity load historical data wereobtained from metrological office of Government of India (GOI),

Pune(India) and state loaddispatch center,Mumbai(India).Field visits to state load

dispatchcenter,Mumbai(India) weredone.2. LITERATURE SURVEY One of the first published studies

was done by Heinemann et al. in 1966 which dealtwith the relationship between temperature and load.

Lijesen and Rosing (1971) developedload orecastig systemwhich used statistical approach. In this study,

estimated average rootmean square error value was 2.1%. Hagan and Behr (1987) forecasted load using a

timeseries model. With this model, the nonlinear relationship between load and temperature dataduring

winter months was clearly observed. Park et al (1991) claimed that the statisticalmethods like regression

and interpolation did not provide reliable prediction performancesthat 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 wasfound successful when compared with the regression method for

24 hour ahead forecasts withan 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 howdifferent models for electricity Load and price forecast have been developed, which are 92

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print),

ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEable to forecast at

one or more time steps ahead. Dong et al (2003) presented an adaptiveneuro-wavelet model for Short

Term Electricity Load Forecast (STLF). Both historicalload and temperature data, which have important

impacts on load level, are used inforecasting by the proposed model. To enhance the forecasting

accuracy by neuralnetworks, the Non-decimated Wavelet Transform (NWT) is introduced to pre-

processthese data. Benaouda et al (2005) looked at wavelet multiscale decomposition basedautoregressive

approach for the prediction of one-hour ahead load based on historicalelectricity load data.Ching-Lai Hor

et al (2005) made use of forecasting model includingboth climate-related and socioeconomic factors that

can be used very simply by utilityplanners to assess long-term monthly electricity patterns using long-

term estimates ofclimate parameters, gross domestic product (GDP), and population growt. Myint et

al(2008) proposed a novel model for short term loadforecast (STLF) in the electricitymarket.In this study

the prior electricity demand data are treated as time series. The modelis composed of several neural

networks whose data are processed using a wavelettechnique. The model is created in the form of a

simulation program written withMATLAB.3. KEY FEATURES OF ARTIFICIAL NEURAL NETWORK

BASED SHORTTERM LOAD FORECASTING (ANNSTLF) Advantages of ANNSTLF1. Adaptive

learning: An ability to learn how to do tasks based on the data given fortraining or initial experience.2.

Self-Organization: An ANN can create its own organization or representation of theinformation it receives

during learning time.3. Real Time Operation: ANN computations may be carried out in parallel, and

specialhardware devices are being designed and manufactured which take advantage of thiscapability.

Limitations in the ANNSTLFSeveral difficulties exist in short-term load forecasting such as precise

hypothesis of theinput-output relationship, generalization of experts’ experience, the forecasting

ofanomalous 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 ofa neural

network. The three basic elements of the neuron model are. A set of weights, anadder for summing the

input signals and activation function for limiting the amplitude ofthe output of a neuron. 93

4. 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 1 Model of

an artificial neural network (ANN)5. NEURAL NETWORK (NN) MODEL WITH WAVELET

ENHANCEMENTFOR TIME SERIES FORECAST To improve the quality of the raw input signal for

time series forecast, theneural network model is enhanced with multi-scale wavelet transform. Figure

belowshows an illustration of the wavelet enhanced neural network model for time seriesforecast.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 forecasting6. MEAN

AVERAGE PERCENTAGE ERROR (MAPE) ; PA = Actual load demand, PF = Forecasted load demand,

N= Number of time sections. 94

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print),

ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEFollowing table

shows the different methods of the electric load forecasting and meanabsolute percentage error based on

literature survey. The figure shows the neural networkmethod is more accurate than any other methods.

Therefore method used in this study i.eartificial 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 Sunday7. RESULTS The results obtained from testing the

trained neural network on new data for 24 hoursof a day over a one-week period are presented below in

graphical form. Each graph shows aplot of both the predicted and actual electrical load in MW values

against the hour of theday. The absolute mean error % (AME %) between the predicted and actual loads

for eachday has been calculated and presented in the table. Overall, these error values translate to

anabsolute mean error of 1.24% for the network. This represents a high degree of accuracy inthe 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 95

6. 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 96

7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print),

ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME 8. Characteristics of

the power system loadVarious factors that influence the system load behavior, can be classified into the

majorcategories as weather, time, economy, random disturbance etc Figure 9 Hour load profile of grid in

this study for a week of March 2010From the above daigram it is seen that ,typically load is low and

stable from 0:00 to 6:00; itrises from around 6:00 to 9:00 and then becomes flat again until around 12:00;

then itdescends gradually until 17:00; thereafter it rises again until 19:00; it descends again until theend of

the day.CONCLUSION The result of adaptive neuro-wavelet time series forecast model used for one

dayahead short term load forecast for the considered area under study in India has a goodperformance

and reasonable prediction accuracy. Its forecasting reliabilities were evaluatedby computing the mean

absolute error between the exact and predicted electrcity loadvalues.We were able to obtain an Absolute

Mean Error (AME) of 1.24% which represents ahigh degree of accuracy. The results suggest that ANN

model with the developed structurecan perform good prediction with least error and finally this neural

network could be animportant tool for short term load forecasting. The accuracy of the electricity load

forecast iscrucial in better power system planing and reliability.ACKNOWELDGEMENT Bharati

Vidyapeeth Deemed University College of Engineering, Pune (India) forproviding MATLAB software and

all necessary lab & library facilities.Metrologicaldepartment of Government of India for weather data.

State load dispatch center, Mumbai(India) for load data. 97

8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print),

ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEREFERENCES[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 98

9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print),

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

2012Field 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 99

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