基于诱导有序加权平均算子和马尔可夫链的中长期电力负荷组合预测模型  

A Combination Model for Medium-and Long-Term Load Forecasting Based on Induced Ordered Weighted Averaging Operator and Markov Chain

作　　者：龙瑞华[1] 毛弋[1] 毛李帆[1] 孙东杰[1] 张芳明[1] 张婷[1] 陈宇哲[1] 

机构地区：[1]湖南大学电气与信息工程学院,湖南省长沙市410082

出　　处：《电网技术》2010年第3期150-156,共7页Power System Technology

摘　　要：针对传统中长期电力负荷组合预测方法的缺陷,将诱导有序加权平均算子(induced ordered weighted averaging,IOWA)与马尔可夫链(Markov chain,MC)相结合构建IOWA-MC组合预测模型。该模型根据每个单项预测方法在各时点拟合精度的高低顺序对其赋权,保证了组合预测模型中权系数与拟合精度在任一时点上的相关性,同时利用MC定性推测出预测时间点上各单项预测方法的预测精度状态,从而确定其在预测时点上的权系数。算例结果表明,IOWA-MC能自动识别高精度预测模型,排除低精度预测方法带来的影响,预测精度较高,具有较强的实用价值。To remedy the defects in traditional combination method for medium- and long-term load forecasting, a method, which combines the induced ordered weighted averaging operator （IOWA） with Markov chain （MC） to construct an IOWA-MC combinative forecasting model, is proposed. According to the order of fitting accuracy of each forecasting method at each time point the proposed model endows them with weights to ensure the correlativity of weight coefficients and fitting accuracy in combinative forecasting model at any time point; meanwhile, by use of MC the forecasted accuracy condition of each forecasting method at the forecasted time point can be qualitatively surmised, thus its weight coefficient at the forecasted time point can be determined. Calculation results show that IOWA-MC combination model can automatically recognize accurate forecasting model and exclude the affect of inaccurate forecasting methods. The proposed method can provide more accurate forecasting results.

关 键 词：诱导有序加权平均算子 马尔可夫链 预测精度 负荷预测 

分 类 号：TM715[电气工程—电力系统及自动化]