Spatio-Temporal Two-Dimensions Data Based Customer Baseline Load Estimation Approach Using LASSO Regression

Abstract

Customer baseline load (CBL) estimation plays a crucial role in financial settlement for incentive-based demand response (DR). Most current CBL estimation methods utilize temporal auto-correlation or spatial cross-correlation to estimate the CBL, which would produce large errors when the load pattern suddenly changes on the DR event day or the number of CONTROL customers is insufficient. To this end, a spatio-temporal CBL estimation approach is proposed to improve the estimation accuracy in this paper. First, all CONTROL customers are grouped into several non-overlapping clusters by the K-means algorithm. Each DR customer is then matched to the most similar cluster according to the similarity between the typical load pattern and the cluster center. Second, spatio-temporal features are extracted from the historical load data of the DR customer itself and the load data of the matched CONTROL customers on the DR event day. Third, the least absolute shrinkage and selection operator (LASSO) regression model is constructed to estimate the CBL of each DR customer. The effectiveness and superiority of the proposed approach have been verified on a real-world load dataset.