Short-term Residential Load Forecasting Based on K-shape Clustering and Domain Adversarial Transfer Network

作     者：Jizhong Zhu Yuwang Miao Hanjiang Dong Shenglin Li Ziyu Chen Di Zhang 

作者机构：School of Electric Power EngineeringSouth China University of TechnologyGuangzhou 510641China Department of Electrical EngineeringThe Hong Kong Polytechnic UniversityHong KongChina 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2024年第12卷第4期

页      面：1239-1249页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(No.52177087) Guangdong Basic and Applied Basic Research Foundation,China(No.2022B1515250006) 

主　　题：Load forecasting domain adversarial K-shape clustering long short-term memory network seq2seq network attention mechanism 

摘      要：In recent years,the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution ***,due to the scarcity of historical data for these new consumers,it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting *** paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering,deep learning,and transfer learning technologies to address this *** begin,this paper leverages the domain adversarial transfer *** employs limited data as target domain data and more abundant data as source domain data,thus enabling the utilization of source do-main insights for the forecasting task of the target ***,a K-shape clustering method is proposed,which effectively identifies source domain data that align optimally with the target domain,and enhances the forecasting ***,a composite architecture is devised,amalgamating attention mechanism,long short-term memory network,and seq2seq *** composite structure is integrated into the domain adversarial transfer network,bolstering the performance of feature extractor and refining the forecasting *** illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method *** the case study,the relative mean square error of the proposed method is within 30 MW,and the mean absolute percentage error is within 2%.A signifi-cant improvement in accuracy,compared with other comparative experimental results,underscores the reliability of the proposed *** findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecast-ing methods.