中文摘要：

应用最小二乘支持向量机进行短期负荷预测,为了体现离负荷预测点越远对负荷预测精度的影响越不明显,即＂近大远小＂的原则.对训练样本横向及纵向引入隶属度,并用留一法优化模型参数,实现参数的自适应选择,从而提高预测的精度.利用某区域电网最新的负荷数据进行仿真预测,并与不加权及其它的方法相比较.结果表明,所提出的方法与传统方法相比提高了预测的精度.

英文摘要：

Weighted least squares fuzzy support-vector-machines method is proposed for short term load forecasting.In order to reflect the characteristic that the nearer data have a greater impact on the predicting value,the membership distribution of a time domain is introduced in a bi-direction,namely,transverse and longitudinal.To overcome the disadvantage of predicting with a fixed coefficient,a fast-leave-one-out method is used to adaptively optimize the parameters on line.The load data from a regional power grid is used for simulating and the applications of different methods are compared.The results show that the proposed method can improve the forecasting accuracy,compared with traditional methods.