Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm

作     者：Pang, Xinfu Sun, Wei Li, Haibo Wang, Yibao Luan, Changfeng 

作者机构：Shenyang Inst Engn Key Lab Energy Saving & Controlling Power Syst Lia Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Co Ltd Yingkou Power Supply Co Yingkou Liaoning Peoples R China 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2022年第8卷

页      面：e1108页

核心收录：

基　　金：National Natural Science Foundation of China Natural Science Foundation of Liaoning Province of China Department of Education of Liaoning Province of China Program for Shenyang High Level Innovative Talents 61773269 2019-KF-03-08 LJKZ1110 RC190042 

主　　题：Artificial bee colony algorithm Gray relational analysis Short-term power load forecasting Similar days Support vector machine 

摘      要：Short-term power load forecasting is essential in ensuring the safe operation of power systems and a prerequisite in building automated power systems. Short-term power load demonstrates substantial volatility because of the effect of various factors, such as temperature and weather conditions. However, the traditional short-term power load forecasting method ignores the influence of various factors on the load and presents problems of limited nonlinear mapping ability and weak generalization ability to unknown data. Therefore, a short-term power load forecasting method based on GRA and ABC-SVM is proposed in this study. First, the Pearson correlation coefficient method is used to select critical influencing factors. Second, the gray relational analysis (GRA) method is utilized to screen similar days in the history, construct a rough set of similar days, perform K-means clustering on the rough sets of similar days, and further construct the set of similar days. The artificial bee colony (ABC) algorithm is then utilized to optimize penalty coefficient and kernel function parameters of the support vector machine (SVM). Finally, the above method is applied on the basis of actual load data in Nanjing for simulation verification, and the results show the effectiveness of the proposed method.