Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used in forecasting load, artificial intelligence techniques provide greater accuracy to the forecasts as compared to conventional techniques. Fuzzy Logic, a very robust artificial intelligent technique, is described in this paper to forecast load on long term basis. The paper gives a general algorithm to forecast long term load. The algorithm is an Extension of Short term load forecasting method to Long term load forecasting and concentrates not only on the forecast values of load but also on the errors incorporated into the forecast. Hence, by correcting the errors in the forecast, forecasts with very high accuracy have been achieved. The algorithm, in the paper, is demonstrated with the help of data collected for residential sector (LT2 (a) type load: Domestic consumers). Load, is determined for three consecutive years (from April-06 to March-09) in order to demonstrate the efficiency of the algorithm and to forecast for the next two years (from April-09 to March-11).
[1] Energy to Energy Policy and Planning Office, Ministry of Energy, Royal
Thai Government, Thailand.
[2] Energy to Energy Policy and Planning Office, "Load forecasting reports
2547", Ministry of Energy, Royal Thai Government, Thailand.
[3] Peter Vas., "Artificial-Intelligence-Based Electrical Machines and
Drives", Oxford science publication, 1999.
[4] Lefteri H.Tsoukalas, Robert E.Uhrig, "Fuzzy and Neural Approaches in
Engineering", A wiley-Interscience publication, JohnWilley&Sons INC,
1997.
[5] Peter M.ills, Albert Y.Zomaya, and Moses O.Tade, "Neuro-Adaptive
Process Control A Practical Approach", John Willey&Sons Ltd, 1996.
[6] R.A. Aliev, R.R.Aliev, "Soft Computing and its Application", World
scientific publication, 2001.
[7] Stamatios V. Kartalopoulos, "Understanding Neural Network and Fuzzy
logic Basic Concepts and Applications", AT&T, 1996.
[8] K. Metaxiotis, A. Kagiannas, D. Askounis, J. Psarras, "Artificial
intelligence in short term electric load forecasting: a state-of-the-art
survey for the research", Energy Conversion and Management 44, 2003,
pp.1525-1534.
[9] Georgr gross,franscisco D.galiana, "Short-term load forecasting",
Preceeding of the IEEE, vol.75,no.12, December 1987, pp.1558-1573.
[10] Tomonoobu senjyu, hitoshi takara,katsumi ueezato,toshihisa funabashi,
"One-hour ahead load forecasting using neural network", IEEE Trans.
power syst., vol.17, no.1, February 2002, pp.113-118.
[11] Zhiyong Wang,Yijia Cao, "Mutual Information and Non-fixed ANNs
for Daily Peak load forecasting", Power Systems Conference and
Exposition, IEEE PES ,2006, pp:1523 - 1527.
[12] wenjin Dai, ping Wang, "Application of pattern recognition and
artificial neural network to load forecasting in electric power system",
Third international conference on natural computation, ICNC 2007.
[13] Hiroyuki mori,eitaro kurata, "Graphical Modeling for Selecting Input
Variables of Short-term Load Forecasting", Power Tech, 2007 IEEE
Lausanne, July 2007, pp:1084 - 1089.
[14] G.A.Adepoju, S.O.A.ogaunjuyigbe, and K.O.Alawode, "Application of
neural network to load forecasting in nigerian electrical power system",
The pacific journal of science and technology, vol.8, no.1, May 2007,
pp.68-72.
[15] Mohsen Hayati, Yazdan Shirvany, "Artificial neural network approach
for short term load forecasting for Illam region", International journal of
electrical, computer, and system engineering, vol.1, no.2, pp.121-125.
[16] H.A.Salama, A.F.AbdElGawad, H.M.Mahmond, E.A.Mohamed,
S.M.Saker, "Short term load forecasting investigations of eqyptian
electrical network using ANNs", Universities Power Engineering
Conference, 2007, pp: 550 - 555.
[17] Bhavesh kumar chauha, amit sharma, and m.hanmandlu, "Neuro-fuzzy
approach based short term electric load forecasting", 2005 IEEE/PES
Transmission and Distribution Conference & Exhibition, Asia and
Pacific Dalian, Chaina.
[18] Z.Y.Wang, C.X.Guo , Y.J.Cao, "A New method for short term load
forecasting integrating fuzzy tough sets with artificial neural network",
Power Engineering Conference, IPEC 2005, pp:1 - 173.
[19] Cuiru wang, Zhikun cui , Qi chen, "Short term load forecasting based
on fuzzy neural network", Intelligent Information Technology
Application, Workshop on 2-3 Dec. 2007, pp:335 - 338.
[20] Hari seetha and R.saravanan, "Short term electric load prediction using
fuzzy BP", Journal of computing and information technology-CIT 15,
2007, pp: 267-283.
[21] Chih-hsien kung, michael J.Devaney, chung-ming huang, chih-ming
kung. "An adaptive power system load forecasting scheme using a
genetic algorithm embedded neural network", IEEE Intrumentation and
Measurement technology conference St.Paul,Minnesota, USA, May
18-20.
[22] L.L.Lai,H.Subasinghe, N.Rajkumar, E.Vaseekar, B.J.Gwyn, V.K.Sood,
"Object oriented genetic algorithm based artificial neural network for
load forecasting", Springer-verlag berlin ,1999, pp:462-469.
[23] Zhao-yang dong, Bai-ling zhang, Qian huang, "A daptive neural
network short term load forecasting with wavelet decompositions",
IEEE Porto power tech conference, 10-13 september,porto,Portugal.
[24] S.Chenthur Pandian, K.Duraiswamy, C.Christer Asir Rajan,
N.Kanagaraj, "Fuzzy approach for short term load forcasting", Electric
Power Systems Research 76, 2006, pp:541-548.
[25] Jian-Chang Lu, Dong-xiao niu, zheng-yuan jia, "A study of short term
load forecasting based on arima-ann", Machine Learning and
Cybernetics, vol.5, 2004, pp:3183 - 3187.
[26] Ummuhan basaran filik, Mehmet kurban, "A new approach for the short
term load forecasting with autoregressive and artificial neural network
models", International journal of computational intelligence research ,
vol.3, no.1, pp:66-71.
[27] P.K.Dash, S.Mishra, S.Dash, A.C.Liew, "Genetic optimization of a self
organizing fuzzy-neural network for load forecasting", IEEE 2000.
[28] Gwo-ching liao, Ta-peng tsao, "Integrated genetic algorithm/Tabu
search and neural fuzzy networks for short-term load forecasting",
Power Engineering Society General Meeting, 2004, pp:1082 - 1087.
[29] Gwo-ching liao, Ta-peng tsao, "Novel GA-Based Approach and Neural
fuzzy networks application in short-term load forecasting", Power
Engineering Society General Meeting, 2004, pp:589-594.
[30] Kyung-bin song, young-sik baek, dug hun hong and gilsoo jang, "Shortterm
load forecasting for the holidays using fuzzy linear regression
method", IEEE Trans. power syst., vol.20, no.1, 2005, pp: 96-101.
[1] Energy to Energy Policy and Planning Office, Ministry of Energy, Royal
Thai Government, Thailand.
[2] Energy to Energy Policy and Planning Office, "Load forecasting reports
2547", Ministry of Energy, Royal Thai Government, Thailand.
[3] Peter Vas., "Artificial-Intelligence-Based Electrical Machines and
Drives", Oxford science publication, 1999.
[4] Lefteri H.Tsoukalas, Robert E.Uhrig, "Fuzzy and Neural Approaches in
Engineering", A wiley-Interscience publication, JohnWilley&Sons INC,
1997.
[5] Peter M.ills, Albert Y.Zomaya, and Moses O.Tade, "Neuro-Adaptive
Process Control A Practical Approach", John Willey&Sons Ltd, 1996.
[6] R.A. Aliev, R.R.Aliev, "Soft Computing and its Application", World
scientific publication, 2001.
[7] Stamatios V. Kartalopoulos, "Understanding Neural Network and Fuzzy
logic Basic Concepts and Applications", AT&T, 1996.
[8] K. Metaxiotis, A. Kagiannas, D. Askounis, J. Psarras, "Artificial
intelligence in short term electric load forecasting: a state-of-the-art
survey for the research", Energy Conversion and Management 44, 2003,
pp.1525-1534.
[9] Georgr gross,franscisco D.galiana, "Short-term load forecasting",
Preceeding of the IEEE, vol.75,no.12, December 1987, pp.1558-1573.
[10] Tomonoobu senjyu, hitoshi takara,katsumi ueezato,toshihisa funabashi,
"One-hour ahead load forecasting using neural network", IEEE Trans.
power syst., vol.17, no.1, February 2002, pp.113-118.
[11] Zhiyong Wang,Yijia Cao, "Mutual Information and Non-fixed ANNs
for Daily Peak load forecasting", Power Systems Conference and
Exposition, IEEE PES ,2006, pp:1523 - 1527.
[12] wenjin Dai, ping Wang, "Application of pattern recognition and
artificial neural network to load forecasting in electric power system",
Third international conference on natural computation, ICNC 2007.
[13] Hiroyuki mori,eitaro kurata, "Graphical Modeling for Selecting Input
Variables of Short-term Load Forecasting", Power Tech, 2007 IEEE
Lausanne, July 2007, pp:1084 - 1089.
[14] G.A.Adepoju, S.O.A.ogaunjuyigbe, and K.O.Alawode, "Application of
neural network to load forecasting in nigerian electrical power system",
The pacific journal of science and technology, vol.8, no.1, May 2007,
pp.68-72.
[15] Mohsen Hayati, Yazdan Shirvany, "Artificial neural network approach
for short term load forecasting for Illam region", International journal of
electrical, computer, and system engineering, vol.1, no.2, pp.121-125.
[16] H.A.Salama, A.F.AbdElGawad, H.M.Mahmond, E.A.Mohamed,
S.M.Saker, "Short term load forecasting investigations of eqyptian
electrical network using ANNs", Universities Power Engineering
Conference, 2007, pp: 550 - 555.
[17] Bhavesh kumar chauha, amit sharma, and m.hanmandlu, "Neuro-fuzzy
approach based short term electric load forecasting", 2005 IEEE/PES
Transmission and Distribution Conference & Exhibition, Asia and
Pacific Dalian, Chaina.
[18] Z.Y.Wang, C.X.Guo , Y.J.Cao, "A New method for short term load
forecasting integrating fuzzy tough sets with artificial neural network",
Power Engineering Conference, IPEC 2005, pp:1 - 173.
[19] Cuiru wang, Zhikun cui , Qi chen, "Short term load forecasting based
on fuzzy neural network", Intelligent Information Technology
Application, Workshop on 2-3 Dec. 2007, pp:335 - 338.
[20] Hari seetha and R.saravanan, "Short term electric load prediction using
fuzzy BP", Journal of computing and information technology-CIT 15,
2007, pp: 267-283.
[21] Chih-hsien kung, michael J.Devaney, chung-ming huang, chih-ming
kung. "An adaptive power system load forecasting scheme using a
genetic algorithm embedded neural network", IEEE Intrumentation and
Measurement technology conference St.Paul,Minnesota, USA, May
18-20.
[22] L.L.Lai,H.Subasinghe, N.Rajkumar, E.Vaseekar, B.J.Gwyn, V.K.Sood,
"Object oriented genetic algorithm based artificial neural network for
load forecasting", Springer-verlag berlin ,1999, pp:462-469.
[23] Zhao-yang dong, Bai-ling zhang, Qian huang, "A daptive neural
network short term load forecasting with wavelet decompositions",
IEEE Porto power tech conference, 10-13 september,porto,Portugal.
[24] S.Chenthur Pandian, K.Duraiswamy, C.Christer Asir Rajan,
N.Kanagaraj, "Fuzzy approach for short term load forcasting", Electric
Power Systems Research 76, 2006, pp:541-548.
[25] Jian-Chang Lu, Dong-xiao niu, zheng-yuan jia, "A study of short term
load forecasting based on arima-ann", Machine Learning and
Cybernetics, vol.5, 2004, pp:3183 - 3187.
[26] Ummuhan basaran filik, Mehmet kurban, "A new approach for the short
term load forecasting with autoregressive and artificial neural network
models", International journal of computational intelligence research ,
vol.3, no.1, pp:66-71.
[27] P.K.Dash, S.Mishra, S.Dash, A.C.Liew, "Genetic optimization of a self
organizing fuzzy-neural network for load forecasting", IEEE 2000.
[28] Gwo-ching liao, Ta-peng tsao, "Integrated genetic algorithm/Tabu
search and neural fuzzy networks for short-term load forecasting",
Power Engineering Society General Meeting, 2004, pp:1082 - 1087.
[29] Gwo-ching liao, Ta-peng tsao, "Novel GA-Based Approach and Neural
fuzzy networks application in short-term load forecasting", Power
Engineering Society General Meeting, 2004, pp:589-594.
[30] Kyung-bin song, young-sik baek, dug hun hong and gilsoo jang, "Shortterm
load forecasting for the holidays using fuzzy linear regression
method", IEEE Trans. power syst., vol.20, no.1, 2005, pp: 96-101.
@article{"International Journal of Information, Control and Computer Sciences:58034", author = "Jagadish H. Pujar", title = "Fuzzy Ideology based Long Term Load Forecasting", abstract = "Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used in forecasting load, artificial intelligence techniques provide greater accuracy to the forecasts as compared to conventional techniques. Fuzzy Logic, a very robust artificial intelligent technique, is described in this paper to forecast load on long term basis. The paper gives a general algorithm to forecast long term load. The algorithm is an Extension of Short term load forecasting method to Long term load forecasting and concentrates not only on the forecast values of load but also on the errors incorporated into the forecast. Hence, by correcting the errors in the forecast, forecasts with very high accuracy have been achieved. The algorithm, in the paper, is demonstrated with the help of data collected for residential sector (LT2 (a) type load: Domestic consumers). Load, is determined for three consecutive years (from April-06 to March-09) in order to demonstrate the efficiency of the algorithm and to forecast for the next two years (from April-09 to March-11).
", keywords = "Fuzzy Logic Control (FLC), Data DependantFactors(DDF), Model Dependent Factors(MDF), StatisticalError(SE), Short Term Load Forecasting (STLF), MiscellaneousError(ME).", volume = "4", number = "4", pages = "771-6", }
