Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (11): 220-223.

### Short-time load prediction based on support vector machine optimized by catfish particle swarm optimization algorithm

SHI Xiaoyan, LIU Huaixia, YU Shuijuan

1. Department of Automation, College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China
• Online:2013-06-01 Published:2013-06-14

### 鲶鱼粒子群算法优化支持向量机的短期负荷预测

1. 安徽理工大学 电气与信息工程学院 自动化系，安徽 淮南 232001

Abstract: 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.