One Hour Ahead Load Forecasting Using Artificial Neural Network for the Western Area of Saudi Arabia
Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330179Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1737
 Henrique Steinherz Hippert, Carlos Eduardo Pedreira, and Reinaldo Castro Souza. "Neural Networks for Short-Term Load Forecasting: A Review and Evaluation". IEEE Transactions on Power Systems, Vol. 16, No. 1, February 2001.
 O.A. Alsayegh. "Short-Term Load Forecasting Using Seasonal Artificial Neural Networks". International Journal of Power and Energy Systems, Vol. 23, No. 3, 2003.
 Tomonobu Senjyu,, Hitoshi Takara, Katsumi Uezato, and Toshihisa Funabashi, "One Hour-Ahead Load Forecasting Using Neural Network", IEEE Transactions on power systems, Vol. 17, NO. 1, February 2002.
 A.G.Baklrtzis, V.Petrldis, S.J.Klartzis, M.C.Alexiadls, A.H.Malssis, "A Neural Network Short Term Load Forecasting Model for the Greek Power System", IEEE Transactions on power systems, vol.11, No2, May 1996.
 Ibrahim Moghram, Saifure Rahman, "Analysis Evaluation of Five Short Term Load Forecasting Techniques", IEEE Transactions on power systems , VOL. 4, NO.4, 1989.
 K.Lru, S.Subbarayan, R.R.Shoults, M.T.Manry, C.Kwan, F.L.lewis, J.Naccarino, "Comparison of very short term load forecasting techniques". IEEE Transactions on power systems, VOL. 11, NO.2, 1996.
 Eugene A. Feinberg, Dora Genethliou. "Applied Mathematics for Power Systems: Load Forecasting".
 Alex D, Timothy C. "A Regression-Based Approach to Short Term System Load Forecasting". IEEE transaction on Power Systems,5,4,1535-1550,1990.
 S. Rahman, 0. Hazim, "A Generalized Knowledge-Based Short-Term Load Forecasting Technique", IEEE Transactions on power systems , Vol. 8, No. 2, May 1993.
 Kun-Long Ho, Yuan-Yih Hsu, Chih-Chien Liang, Tsau-Shin Lai "Short-Term Load Forecasting of Taiwan Power System Using A Knowledge-Based Expert Systems", IEEE Transactions- on power systems , Vol. 5, No. 4, Nov 1990.
 H.Chen, C.A. Canizares, and A. Singh. "ANN-Based Short-Term Load Forecasting in Electricity Markets". Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference,2:411- 415, 2001.
 D.C. Park, M.A.Elsharqawi, R.J.Marks II. "ELECTRIC Load Forecasting using Artifical Neural Network", IEEE Transaction on Power System, 6, 1991.
 Hagan, M.T., H.B. Demuth, and M.H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.
 Simon Haykin. "Neural Network A Comprehensive Foundation". Prentice Hall International, Second edition.
 Tomonobu Senjyu, Paras Mandal, Katsumi Uezato, and Toshihisa Funabashi. "Next Day Load Curve Forecasting, Using Hybrid Correction Method", IEEE Transactions on power systems, Vol. 20, No. 1, February 2005.
 D.J.C.MacKay, "Bayesian Interpolation," Neural Computation, vol. 4, pp415-447, 1992.
 Dan Foresee, F. Hagan, M.T." Gauss-Newton approximation to Bayesian learning," International Conference on Neural Networks, vol.3, pp1930-1935, 1997.