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

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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



  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.

Key words: short-time load, support vector machine, chaotic theory, particle swarm optimization algorithm, catfish effect

摘要: 为了准确、有效地预测短期负荷,提出了一种鲶鱼粒子群算法优化支持向量机的短期负荷预测模型(BFPSO-SVM)。基于混沌理论对短期负荷时间序列进行相空间重构;将支持向量机参数的组合看作一个粒子位置串,通过粒子间互作找到最优支持向量机参数,并引入“鲶鱼效应”,克服基本粒子群算法的缺点;根据最优参数建立短期负荷预测模型,并对模型性能进行仿真测试。仿真结果表明,相对于其他预测模型,BFPSO-SVM不仅加快了支持向量机参数寻优速度,而且提高了短期负荷预测精度,更适用于短期负荷预测的需要。

关键词: 短期电力负荷, 支持向量机, 混沌理论, 粒子群算法, 鲶鱼效应