摘要
提出了复Gaussian小波SVM模型,并将其应用于对电力系统短期负荷的预测。证明了复Gaussian小波核满足SVM平移不变核条件,建立了相应的SVM,并且使用搜寻者优化算法对相关参数进行优化选择。在短期负荷预测的仿真实验中,通过与常用的径向基核SVM模型的对比,验证了该方法具有较好的精确度和有效性,有一定的实用价值。
A new model of short - term load forecasting （STLF） based on complex Gaussian wavelet support vector machine （ CGW - SVM） is presented. It is proved that the complex Gaussian wavelet is an admissible translation - invariant kernel function of support vector machine （SVM）. CGW -SVM is constructed and its parameters are optimized using seeker optimization algorithm （SOA）. The comparison results for STLF show that the proposed method has better performance than the conventional radial basis function SVM （RBF -SVM） in effectiveness and accuracy and is promising in STLF problem.
出处
《四川电力技术》
2009年第2期58-61,94,共5页
Sichuan Electric Power Technology
