基于PCA-SVM模型的中长期电力负荷预测

电力系统负荷预测模型可以分为单一指标模型和信息集(多指标)模型,为客观准确地对中长期负荷预测进行研究,就要避免重要信息的遗漏,尽可能多的考虑与其有关联的信息。针对信息集模型中的大量信息,通过主成分分析法把综合信息集简化为少数几个主成分,降低了数据的维数;引入核函数和对偶技巧对支持向量机算法进行改进,有效避免了维数灾难和目标函数不可微的问题。通过标准SVM和PCA-SVM模型仿真对比,验证PCA-SVM模型预测结果更为准确,所提方法具有一定的实用性和有效性。

The Medium And Long Term Power Load Forecasting Model Based On PCA-SVM

Power system load forecasting model can be divided into the single index model and information collection model.To insure the accuracy, it is necessary to avoid missing the important information and must collect the related indicators as much as possible. This paper used principal component analysis(PCA) to simplify information.Data correlation removed and data dimension reduced through normalization processing.By introducing the kernel function and the dual skills of the support vector machine algorithm, can effectively avoid the curse of dimensionality.Compared to the SVM method, the accuracy of load forecasting is effectively improved.