Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

作     者：Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam 

作者机构：Department of Electrical and Electronics EngineeringVignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer ApplicationsGovernment Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication EngineeringKalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and EngineeringVignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data ScienceNoroff University CollegeKristiansandNorway Department of ICT ConvergenceSoonchunhyang UniversityKorea Department of Computer Science and EngineeringSoonchunhyang UniversityKorea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：1473-1487页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724 The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund 

主　　题：Energy trading distributed systems power generation load forecasting deep learning peer-to-peer 

摘      要：With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.