Yuhong Zhao
School of Electric Engineering, University of south China, Hengyang, Hunan, China
Lin Lei
Institute of Environmental Protection and Safety Engineering, University of south China, Hengyang, Hunan, China
Yifa Sheng
School of Electric Engineering, University of south China, Hengyang, Hunan, China
ABSTRACT
The development of smart grid and electricity market requires more accurate and faster short-term load forecasting. Aiming at the problems of Radial Basis Function (RBF) network in electric system short term load forecasting, a novel algorithm integrated the advantages of RBF and Quantum Particle Swarm Optimization algorithm (QPSO) is proposed to improve the short-term load forecasting accuracy and speed. In this study, radial basis function network is trained by QPSO. After confirmed the nodes number of hidden layer, all network parameters are coded to individual particles to optimize learning algorithm. Then, the parameter can search optimal-adaptive value in global space. Using the optimized network to forecast load, the case analysis shows that, compared with the traditional network method, the new algorithm has better predictive ability on power system short-term load due to the higher predict precision and faster convergence.
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How to cite this article
Yuhong Zhao, Lin Lei and Yifa Sheng, 2013. Application of Radial Basis Function Optimized by Quantum Particle Swarm Optimization Algorithm in Electric Power System. Information Technology Journal, 12: 6475-6480.
DOI: 10.3923/itj.2013.6475.6480
URL: https://scialert.net/abstract/?doi=itj.2013.6475.6480
DOI: 10.3923/itj.2013.6475.6480
URL: https://scialert.net/abstract/?doi=itj.2013.6475.6480
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