Probabilistic load forecasting（PLF） is able to present the uncertainty information of the future loads. It is the basis of stochastic power system planning and operation. Recent works on PLF mainly focus on how to develop and combine forecasting models, while the feature selection issue has not been thoroughly investigated for PLF.This paper fills the gap by proposing a feature selection method for PLF via sparse L1-norm penalized quantile regression. It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection. Since both the number of training samples and the number of features to be selected are very large, the feature selection process is casted as a large-scale convex optimization problem. The alternating direction method of multipliers is applied to solve the problem in an efficient manner. We conduct case studies on the open datasets of ten areas. Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.
类型: 期刊论文
作者: Yi WANG,Dahua GAN,Ning ZHANG,Le XIE,Chongqing KANG
来源: Journal of Modern Power Systems and Clean Energy 2019年05期
年度: 2019
分类: 工程科技Ⅱ辑
专业: 电力工业
单位: Department of Electrical Engineering, Tsinghua University,Department of Electrical and Computer Engineering, Texas A&M University
基金: supported by National Key R&D Program of China (No. 2016YFB0900100)
分类号: TM715
页码: 1200-1209
总页数: 10
文件大小: 1134K
下载量: 11
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