%0 Journal Article %A SHI Xiaoyan %A LIU Huaixia %A YU Shuijuan %T Short-time load prediction based on support vector machine optimized by catfish particle swarm optimization algorithm %D 2013 %R %J Computer Engineering and Applications %P 220-223 %V 49 %N 11 %X In order to accurately, effectively predict short-term load, this paper proposes a short-term load prediction(BFPSO-SVM) based on support vector machine optimized by catfish particle swarm optimization algorithm. The short-term load time series are reconstructed based on chaos theory, and then the support vector machine SVM parameters are taken as a particle location string, and catfish effect is introduced to overcome the shortcomings of particle swarm algorithm to find the optimal parameters of support vector machine through the particle interactions, short-term load forecasting model is built according to the optimum para-meters and the model performance is tested by simulation experiment. The simulation results show that, compared with other prediction models, BFPSO-SVM accelerates the parameters optimizing speed of support vector machine and improves the prediction precision of short term load, and it is more suitable for short-term load prediction needs. %U http://cea.ceaj.org/EN/abstract/article_30537.shtml