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.
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Niu, D., Gu, Z., Zhang, Y. (2009). An AFSA-TSGM Based Wavelet Neural Network for Power Load Forecasting. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_114
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DOI: https://doi.org/10.1007/978-3-642-01513-7_114
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