Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30680
New Approach for Load Modeling

Authors: S. Chokri

Abstract:

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: Machine Learning, Neural Network, Load Forecasting, fuzzy inference, fuzzy modeling and rule extraction, Support Vector Regression

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099555

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1799

References:


[1] E. Gonzalez-Romera, M.A. Jaramillo-Moran, D. Carmona-Fernandez, Monthly electric energy demand forecasting based on trend extraction, IEEE Transactions on Power Systems 21 (4) (2006) 1946--1953.
[2] A.D. Papalexopoulos, T.C. Hesterberg, A regression-based approach to short term load forecasting, IEEE Transactions on Power Systems 5 (4) (1990) 1535--1550.
[3] S. Rahman, O. Hazim, A generalized knowledge-based short term load forecasting technique, IEEE Transactions on Power Systems 8 (2) (1993) 508--514.
[4] S.J. Huang, K.R. Shih, Short-term load forecasting via ARMA model identification including non-Gaussian process considerations, IEEE Transactions on Power Systems 18 (2) (2003) 673--679.
[5] H. Wu, C. Lu, A data mining approach for spatial modeling in small area load forecast, IEEE Transactions on Power Systems 17 (2) (2003) 516-- 521.
[6] G.E.P. Box, G.M. Jenkins, Time Series Analysis Forecasting and Control, Holden-Day, San Francisco, CA, 1976.
[7] A. Khotanzad, R.C. Hwang, A. Abaye, D. Maratukulam, An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities, IEEE Transactions on Power Systems 10 (3) (1995) 1716--1722.
[8] T. Czernichow, A. Piras, K. Imhof, P. Caire, Y. Jaccard, B. Dorizzi, A. Germond, Short term electrical load forecasting with artificial neural networks, Engineering Intelligent Systems for Electrical Engineering and Communications 2 (1996) 85--99.
[9] L.M. Saini, M.K. Soni, Artificial neural network-based peak load forecasting using conjugate gradient methods, IEEE Transactions on Power Systems 12 (3) (2002) 907--912.
[10] S. Fan, C.X. Mao, L.N. Chen, Peak load forecasting using the selforganizing map, in: Advances in Neural Networks (ISNN) 2005, Pt. III, Springer-Verlag, New York, 2005, pp. 640--649.
[11] B.-J. Chen, M.-W. Chang, C.-J. Lin, Load forecasting using support vector machines: a study on EUNITE competition 2001, IEEE Transactions on Power Systems 19 (4) (2004) 1821--1830.
[12] S. Fan, C.X. Mao, L.N. Chen, Next-day electricity price forecasting using a hybrid network, IET Generation, Transmission and Distribution 1 (1) (2007) 176--182.
[13] C. Cortes, V. Vapnik, Support-vector network, Machine Learning 20 (1995) 273--297.
[14] L. A. Zadeh, The concept of linguistic variable and its application to approximate reasoning, Inform. Sci., vol. 8, pp. 199-249, 1975.
[15] C Slim (2009) : Hybrid Approach in Neural Network design Applied to Financial Time Series Forecasting, The Journal of American Academy of Business Cambridge, Vol. 15, Num. 1,September 2009, 294-300.
[16] C. Slim (2007) : Fuzzy Neural Model for Bankruptcy Prediction, The Journal of Business Review Cambridge, Vol. 8, Num. 2, December 2007, 117-122.