基于支持向量机的中长期电力负荷预测研究与应用

采用一种新的机器学习方法——支持向量机,建立了中长期电力负荷预测模型.阐述了支持向量机的基本内容,对影响电力负荷诸多因素的样本集进行了标准化处理和主因素分析(PCA).采用Libsvm训练了数据集,并与灰色预测GM(1,1)模型、多元线性回归模型、模糊ISODATA聚类模型和BP神经网络进行对比.结果表明,此算法有更高的准确性,可为电力负荷预测提供有效依据.

Study and application of support vector machine to forecast mid-long-term electric power load

A new machine learning method-support vector machine(SVM)was introduced and some of its characteristics were discussed.After linearly scaling and principal components analyzing(PCA), the data that affect the mid-long-term electric power load were trained by the tools of Libsvm.Com- pared to GM(1,1),multi-variables regression,BP neural network and fuzzy clustering-ISODATA,the result of SVM is excellent.Based on the model presented the mid-long-term load forecasting can be im- proved.