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

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