Feature selection for probabilistic load forecasting via sparse penalized quantile regression

• 作者: Chongqing KANG Dahua GAN Le XIE Ning ZHANG Yi WANG
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: PROBABILISTIC load forecasting Feature selection Alternating direction method of multipliers(ADMM) QUANTILE regression L1-norm PENALTY
• 年，卷（期）: 2019,（5）
• 页码范围: 1200-1209
• 页数: 10页
• 分类号: TM715

摘要

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.