基于改进的PSO-SVM的短期电力负荷预测

提出一种基于PSO-SVM电力负荷短期预测方法,在SVM学习过程中引入粒子群算法。通过选取组合核函数来改进SVM算法,这样可以充分保证计算速度和较高的预测精度。利用吉林地区的历史负荷数据作为训练样本,通过与传统的SVM预测模型进行对比,对预测结果与实际数据进行比较,证明基于组合核函数预测方法在一定程度上能够保证短期负荷预测的精度。

Improved PSO-SVM based on short-term power load forecasting

This paper proposes a PSO-SVM short-term power load forecasting method based on the introduction of particle swarm algorithm SVM learning process. To improve SVM kernel function by selecting a portfolio, so you can fully guarantee the computing speed and high prediction accuracy. In this paper, historical load data Jilin region as training samples, with the traditional SVM prediction model by comparing the predicted results were compared with the actual data to prove that the combination forecasting method based on kernel function to some extent able to guarantee the accuracy of short-term load forecasting.