Vol.71, No.1, 2022, pp.1473-1487, doi:10.32604/cmc.2022.022110
OPEN ACCESS
ARTICLE
Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM
  • Pudi Sekhar1, T. J. Benedict Jose2, Velmurugan Subbiah Parvathy3, E. Laxmi Lydia4, Seifedine Kadry5, Kuntha Pin6, Yunyoung Nam7,*
1 Department of Electrical and Electronics Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam, 530049, India
2 Department of Computer Applications, Government Arts & Science College, Kanyakumari, 629401, India
3 Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India
4 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam, 530049, India
5 Department of Applied Data Science, Noroff University College, Kristiansand, Norway
6 Department of ICT Convergence, Soonchunhyang University, Korea
7 Department of Computer Science and Engineering, Soonchunhyang University, Korea
* Corresponding Author: Yunyoung Nam. Email:
Received 27 July 2021; Accepted 27 August 2021; Issue published 03 November 2021
Abstract
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 grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peer-to-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At 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) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The 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 rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In 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 outcome. The 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%.
Keywords
Energy trading; distributed systems; power generation; load forecasting; deep learning; peer-to-peer
Cite This Article
Sekhar, P., J., T., Parvathy, V. S., Lydia, E. L., Kadry, S. et al. (2022). Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM. CMC-Computers, Materials & Continua, 71(1), 1473–1487.
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