基于支持向量机的电力短期负荷预测研究

Research on power short-term load forecasting based on SVM

以城市电力负荷预测为应用背景,根据电力负荷的特点和支持向量机(SVM)方法在解决小样本学习问题中的优势,提出基于SVM的电力短期负荷预测模型,并使用粒子群优化算法优化其参数。基于SVM的电力短期负荷预测模型的运行结果与BP神经网络模型对比表明,前者稳定性好,运行速度快,准确率高。

SVM is based on the principle of structure risk minimization as opposed to the principle of Empirical Risk Minimization supported by conventional regression techniques.For the characteristics of the short-term load forecasting and the advantages of support vector machine （SVM）in solving the learning problem with fewer samples,a short-term load forecasting model based on SVM is presented,in which the parameters in SVM are optimized by particle swarm optimizer （PSO）.Results comparison between the proposed model and the BP neural networks model show that the short term load forecasting model based on SVM has a better stability ,faster running speed and high forecasting precision.