Variable-structure neural network with real-coded genetic algorithm and its application on short-term load forecasting

This paper presents a novel neural network with a variable structure, which is trained by a real-coded genetic algorithm (RCGA), and its application on short-term load forecasting. The proposed variable-structure neural network (VSNN) consists of a Neural Network with Link Switches (NNLS) and a Network Switch Controller (NSC). In the NNLS, switches are introduced in the links between the hidden and output layers. By using the NSC to control the on-off states of the switches in the NNLS, the proposed neural network can model different input patterns with variable network structures. It gives better results and learning ability than the fixed-structure network with link switches (FSNLS) (3), wavelet neural network (WNN) (25) and feed-forward fully-connected neural network (FFCNN) (9). In this paper, an improved RCGA (2) is used to train the parameters of the VSNN. An industrial application on short-term load forecasting in Hong Kong is given to illustrate the merits of the proposed network.

[1]  R. Buizza,et al.  Neural Network Load Forecasting with Weather Ensemble Predictions , 2002, IEEE Power Engineering Review.

[2]  S. Yao,et al.  Evolving wavelet neural networks for function approximation , 1996 .

[3]  Kwang Y. Lee,et al.  Approximate Loading Margin Methods Using Artificial Neural Networks in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[4]  Duc Truong Pham,et al.  Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks , 2011 .

[5]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[6]  Hak-Keung Lam,et al.  On interpretation of graffiti commands for eBooks using a neural network and an improved genetic algorithm , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[7]  Ruey-Hsun Liang,et al.  Short-term load forecasting by a neuro-fuzzy based approach , 2002 .

[8]  Chia-Yon Chen,et al.  Regional load forecasting in Taiwanapplications of artificial neural networks , 2003 .

[9]  T. Funabashi,et al.  One-Hour-Ahead Load Forecasting Using Neural Networks , 2002 .

[10]  W. Charytoniuk,et al.  Very short-term load forecasting using artificial neural networks , 2000 .

[11]  Saifur Rahman,et al.  Short-term load forecasting with local ANN predictors , 1999 .

[12]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[13]  Sai-Ho Ling,et al.  An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations , 2007, Soft Comput..

[14]  Kuihe Yang,et al.  Design of short-term load forecasting model based on fuzzy neural networks , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[15]  A. C. Liew,et al.  Short term load forecasting using genetic algorithm and neural networks , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[16]  James A. Momoh,et al.  Artificial neural network based load forecasting , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[17]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (2nd, extended ed.) , 1994 .

[18]  Kishan G. Mehrotra,et al.  Sunspot numbers forecasting using neural networks , 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Terrence L. Fine,et al.  Neural-network design for small training sets of high dimension , 1998, IEEE Trans. Neural Networks.

[21]  F.H.F. Leung,et al.  A novel GA-based neural network for short-term load forecasting , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[22]  Marco P. Schoen,et al.  Intelligent optimization techniques, genetic algorithms, tabu search, simulated annealing, and neural networks, D. T. Pham and D. Karaboga, Springer: Berlin, Heidelberg, New York; Springer London: London, 2000, 302pp, ISBN 1‐85233‐028‐7 , 2005 .

[23]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[24]  T. Funabashi,et al.  Next day load curve forecasting using hybrid correction method , 2005, IEEE Transactions on Power Systems.