Performance of the novel rough fuzzy-neural network on short-term load forecasting

A hybrid model integrating with rough set theory and fuzzy neural network is presented for short-term load forecasting. A multiobjective genetic algorithm is used to learn automatically the knowledge of historical data set and find the best factors that are relevant to electric loads, and the crude domain knowledge extracted from the elementary data set is applied to design the structure and weights of the neural network. Simulation results demonstrate that the rough fuzzy neural network has better precision and convergence than the traditional fuzzy neural network. Moreover, it becomes easier to understand the transferring way of knowledge in neural network.

[1]  J. Theocharis,et al.  A novel approach to short-term load forecasting using fuzzy neural networks , 1998 .

[2]  Andrzej Skowron,et al.  Rough Sets: A Tutorial , 1998 .

[3]  Feng Li,et al.  Short-term load forecasting based on a rough fuzzy-neural network , 2004, 2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791).

[4]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[5]  Zhong-Xian Chi,et al.  Application of rough set theory and artificial neural network for load forecasting , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[6]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[7]  Sankar K. Pal,et al.  Rough fuzzy MLP: knowledge encoding and classification , 1998, IEEE Trans. Neural Networks.

[8]  Sankar K. Pal,et al.  Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation , 2003, IEEE Trans. Knowl. Data Eng..

[9]  G. Lambert-Torres,et al.  Application of rough sets in power system control center data mining , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

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

[11]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[12]  Roman Słowiński,et al.  Intelligent Decision Support , 1992, Theory and Decision Library.