基于模糊回归支持向量机的短期负荷预测  

Short Term Load Forecasting Based on Fuzzy Regression Support Vector Machine

作　　者：龚灯才[1] 孙长银[1] 李林峰[1] 

机构地区：[1]河海大学电气工程学院,南京210098

出　　处：《三峡大学学报（自然科学版）》2006年第1期37-40,共4页Journal of China Three Gorges University：Natural Sciences

基　　金：国家自然科学基金重大项目(50595412);中国博士后基金(2004036125)

摘　　要：支持向量机(SVM)是一种新颖的机器学习方法,具有泛化能力强、全局最优和计算速度快等突出优点.模糊数学在不确定性、不精确性及噪声引起的问题上,有其特有的计算分析操作,能有效地分析和处理模糊信息.研究了一种模糊回归支持向量机模型,该模型将两者有机结合,发挥了各自的优点.将其应用到电力系统短期负荷预测,仿真结果表明,所提方法不仅具有与支持向量机方法相同的预测精度,且提供了更多的有用信息.The support vector machine （SVM） is a novel type of learning machine which has some remarkable characteristics such as good generalization performance, the absence of local minima and fast computing speed. Fuzzy mathematics is an effective tool in analyzing and treating fuzzy information in these problems with uncertainty or arisen from noise. Researches a novel support vector fuzzy regression machine that com hines the merits of both models. It is applied to short term load forecasting; and the simulation results show that the proposed method can provide forecast precision as normal SVM and supply more useful information.

关 键 词：支持向量机 回归估计 模糊 短期负荷预测 

分 类 号：TM734[电气工程—电力系统及自动化]