一种模糊支持向量的负荷混沌时间序列预测法

根据电网负荷混沌性的特点,提出一种基于模糊支持向量的核回归方法进行电力系统的负荷预测。同时提出多参数同步优化策略,增强了该方法的实用性和有效性。从理论上分析了小样本条件下,可以有效避免过学习的原因,该方法不需设计网络结构,降低了对实验人员经验的依赖程度。选取实际负荷时间序列数据,通过与神经网络法进行对比实验,结果显示出该方法的优越性和适用性,具有较好的实用价值和应用前景。

Chaotic Time Series Method for Load Forecasting Based on Fuzzy Support Vector

According to the chaotic characteristic of power load,fuzzy support vector based kernel regression method is proposed for load forecasting.Then,multi-parameter synchronous optimization strategy is presented to speed up the optimization process.Case study shows that the method has lower error and can avoid over-fitting effectively,which is better than conventional neural network method.It is of great value for engineering application.