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Erschienen in:
2009 | OriginalPaper | Buchkapitel
An AFSA-TSGM Based Wavelet Neural Network for Power Load Forecasting
verfasst von : Dongxiao Niu, Zhihong Gu, Yunyun Zhang
Erschienen in: Advances in Neural Networks – ISNN 2009
Verlag: Springer Berlin Heidelberg
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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.