An AFSA-TSGM Based Wavelet Neural Network for Power Load Forecasting

Authors:
Dongxiao Niu;Zhihong Gu;Yunyun Zhang
Affiliations:
School of Business Administration, North China Electric Power University, Beijing, China 102206;School of Business Administration, North China Electric Power University, Beijing, China 102206;College of Economics Management, North China Electric Power University, Baoding, China 071003
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

An intelligent methodology for power load forecasting was developed. In this forecasting system, wavelet neural network techniques were used in combination with a new evolutionary learning algorithm. The new evolutionary learning algorithm introduced the Tabu Search Algorithm and Genetic Mutation Operator into Artificial Fish Swarm Algorithm (AFSA) to construct a hybrid optimizing algorithm, and is thus called ASFA-TSGM. The hybrid algorithm can greatly improve the ability of searching the global excellent result and the convergence property and accuracy. The effectiveness of the ASFA-TSGM based WNN was demonstrated through the power load forecasting. The simulated results show its feasibility and validity.