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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Niu, D., Gu, Z., & Zhang, Y. (2009). An AFSA-TSGM based wavelet neural network for power load forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5553 LNCS, pp. 1034–1043). https://doi.org/10.1007/978-3-642-01513-7_114
Mendeley helps you to discover research relevant for your work.