基于加权偏最小二乘回归的中长期负荷预测  

Mid-long term load forecasting based on weighted partial least squares regression

作　　者：陈素玲[1] 姚建刚[1] 龚磊[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《电力需求侧管理》2014年第1期21-24,共4页Power Demand Side Management

摘　　要：针对中长期负荷预测,考虑各历史样本在建立适用于预测对象的模型时处于不同的地位,应分配不同的权值,提出一种基于加权偏最小二乘回归(weighted partial least squares regression,WPLSR)的预测方法。利用相似离度计算历史样本与预测对象的相似度,判定样本是否含有异常值,自适应地为历史样本分配权值,进而采用偏最小二乘回归(partial least squares regression,PLSR)提取主成分和回归分析。算例结果表明WPLSR方法的预测精度比普通PLSR模型有显著提高,具有良好的可行性和有效性。Considering that historical load samples each have asymmetrical status and should be assigned to different weightings in the med-long-term load forecasting, this paper pro-poses a weighted partial least squares regression（WPLSR）algo-rithm. The specific modeling procedures are：Analog Deviation be-tween the historical samples and predicting samples is computed;identify abnormal samples; adjust sample weights; partial least squares regression analysis. Experimental results show that the pre-diction accuracy of the weighted partial least squares regression （WPLSR）algorithm is remarkably higher than that of traditional PLSR model. the proposed modeling method is practicable and ef-fective.

关 键 词：中长期负荷预测 偏最小二乘回归 相似离度 权值 

分 类 号：TM715[电气工程—电力系统及自动化] F407.61[经济管理—产业经济]