Abstract
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 L_1-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.
Highlights
The electrical load is affected by various factors such as weather condition, distributed renewable integration, demand response implementation, energy policy, emergent events, etc
The decomposed parts are forecasted by quantile regression neural network (QRNN), quantile random forest, or quantile gradient boosting regression tree (QGBRT); while the dependencies between load and PV uncertainties are modelled by copula function and describe dependent convolution (DDC) [5]
The final stable values of average quantile score (AQS) corresponding to the case of ÀlgðkÞ 1⁄4 4 can be viewed as the performance of the model without feature selection
Summary
The electrical load is affected by various factors such as weather condition, distributed renewable integration, demand response implementation, energy policy, emergent events, etc. We enrich the PLF feature selection literature by proposing a novel sparse penalized quantile regression method. The main contributions of this paper can be summarized as follows: 1) Proposing a feature selection method for PLF by introducing L1-norm sparse penalty into quantile regression model. The reminder of this paper is organized as follows: Section 2 provides the dataset used and the regression models for load forecasting; Section 3 introduces a straightforward feature selection method which is used as benchmark in the case studies; Section 4 introduces the proposed feature selection method and the ADMM-based training method; Section 5 presents the implementation details of our proposed method; and Section 6 conducts the case studies on the open datasets from Global Energy Forecasting Competition in the year of 2012 (GEFCom 2012)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
