基于混沌类电磁算法优化支持向量机的短期负荷预测

Short Term Load Forecasting Based on SVM and Chaos Electromagnetic Algorithm

短期负荷预测准确性对于电网态势感知和电网策略具有十分重要的意义。提出一种基于混沌类电磁学(CEM)优化支持向量机的短期负荷预测方法,该方法利用聚类思想判断数据质量并进行相关数据预处理工作。建立支持向量机的短期负荷预测模型,针对传统支持向量机参数选择困难问题,引入混沌类电磁学算法优化参数,提高算法收敛效率和寻优能力。仿真结果表明:所提算法较传统支持向量机算法和粒子群-支持向量机算法(PSO-SVM)收敛速度更快,寻优能力更强,适用于短期负荷预测。

The accuracy of short-term load forecasting is very important for power grid situation awareness and power grid strategy. A short-term load forecasting method based on chaotic electromagnetics(CEM)optimization support vector machine(SVM)is proposed. This method uses clustering idea to judge the data quality and preprocess the related data. A short-term load forecasting model of SVM is established. Aiming at the difficult problem of parameter selection of traditional SVM,a new method is introduced.Chaotic electromagnetism algorithm optimizes parameters,and improves the convergence efficiency and optimization ability of the algorithm. Simulation results show that the proposed algorithm has faster convergence speed and stronger optimization ability than the traditional support vector machine algorithm and particle swarm optimization support vector machine(PSO-SVM),and is suitable for short-term load forecasting.