Short-Term Peak Load Forecasting: Statistical Methods Versus Artificial Neural Networks

Two practical techniques: Time Series (TS) and Artificial Neural Networks (ANN), for the one-step-ahead short-term peak load forecasting have been proposed and discussed in this paper. We use weather variables since it is well known that better forecasting performances can be obtained taking them into account. The order selection of TS and the number input neurons of the ANN have are based on the computation of correlation functions. Their performances are evaluated through a simulation study. An extensive test activity of the two techniques shows that have better forecasting accuracy and robustness ANN models.

[1]  Francisco Sandoval,et al.  Electric Load Forecasting with Genetic Neural Networks , 1997, ICANNGA.

[2]  Richard A. Davis,et al.  Time Series: Theory and Methods (2nd ed.). , 1992 .

[3]  Osama A. Mohammed,et al.  Practical experiences with an adaptive neural network short-term load forecasting system , 1995 .

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  Paul A. Fishwick,et al.  Feedforward Neural Nets as Models for Time Series Forecasting , 1993, INFORMS J. Comput..

[6]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[7]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[8]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[9]  D. Rumelhart,et al.  Generalization by weight-elimination applied to currency exchange rate prediction , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[10]  Marie Cottrell,et al.  Neural modeling for time series: A statistical stepwise method for weight elimination , 1995, IEEE Trans. Neural Networks.

[11]  Tom Murray,et al.  Predicting sun spots using a layered perceptron neural network , 1996, IEEE Trans. Neural Networks.

[12]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[13]  Magdy M. A. Salama,et al.  Application of the decomposition technique for forecasting the load of a large electric power network , 1996 .

[14]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .