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Short-term Load Forecasting based on Support Vector Machine Optimized by Catfish Particle Swarm Optimization Algorithm

Xiaoyan SHi, Zhenbi Li


In order to accurately, effectively forecast short-term load, a short-term load forecasting based on support vector machine optimized by catfish particle swarm optimization algorithm (CFPSO-SVM) is proposed. First, the short-term load time series is 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. Finally, short-term load forecasting model is built according to the optimum parameters. The model performance is test by simulation experiment and the results show that, compared with other forecasting models, CFPSO-SVM accelerates the parameters optimizing speed of support vector machine and improves the forecasting precision of short term load, and it is more suitable for short-term load forecasting needs.


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

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