Auto-regressive Recurrent Neural Network Approach for Electricity Load Forecasting

this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects the demand of energy load. So the AR-MCRN is used to learn the relationship between past, previous, and future exogenous and endogenous variables. Experimental results show that using the change in weather components and the change that occurred in past load as inputs to the AR-MCRN, rather than the basic weather parameters and past load itself as inputs to the same network, produce higher accuracy of predicted load. Experimental results also show that using exogenous and endogenous variables as inputs is better than using only the exogenous variables as inputs to the network. Keywords—Daily Peak Load Forecasting, Neural Networks, Recurrent Neural Networks, Auto Regressive Multi-Context Neural Network

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

[2]  Georg Dorffner,et al.  Neural Networks for Time Series Processing , 1996 .

[3]  S. J. Kiartzis,et al.  A neural network short term load forecasting model for the Greek power system , 1996 .

[4]  Michel Verleysen,et al.  Prediction of electric load using Kohonen maps - Application to the Polish electricity consumption , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[5]  Zhao Yang Dong,et al.  An adaptive neural-wavelet model for short term load forecasting , 2001 .

[6]  Dagmar Niebur,et al.  Application of Artificial Neural Networks to Power Systems , 2009 .

[7]  Saifur Rahman.  Formulation and analysis of a rule-based short-term load forecasting algorithm , 1990 .

[8]  E. M. Anagnostakis,et al.  Short-term load forecasting based on artificial neural networks parallel implementation , 2002 .

[9]  J J Hopfield,et al.  Learning algorithms and probability distributions in feed-forward and feed-back networks. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Saifur Rahman,et al.  Input variable selection for ANN-based short-term load forecasting , 1998 .

[11]  M. Tahar Kechadi,et al.  A Practical Approach for Electricity Load Forecasting , 2005, WEC.

[12]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[13]  Ming-Wei Chang,et al.  EUNITE Network Competition: Electricity Load Forecasting , 2001 .