Staff View: Optimal load forecasting model for Peer-to-Peer energy trading in smart grids

Optimal load forecasting model for Peer-to-Peer energy trading in smart grids

Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P m...

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Main Authors:	Varghese, Lijo Jacob, Dhayalini, K., Jacob, Suma Sira, Ali, Ihsan, Abdelmaboud, Abdelzahir, Eisa, Taiseer Abdalla Elfadil
Format:	Article
Published:	Tech Science Press 2022
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/33611/
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spelling	my.um.eprints.336112022-07-29T05:26:43Z http://eprints.um.edu.my/33611/ Optimal load forecasting model for Peer-to-Peer energy trading in smart grids Varghese, Lijo Jacob Dhayalini, K. Jacob, Suma Sira Ali, Ihsan Abdelmaboud, Abdelzahir Eisa, Taiseer Abdalla Elfadil QA75 Electronic computers. Computer science Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm (MOGOA) with Deep Extreme Learning Machine (DELM)-based short-term load predictive technique i.e., MOGOA-DELM model for P2P Energy Trading (ET) in SGs. The proposed MOGOA-DELM model involves four distinct stages of operations namely, data cleaning, Feature Selection (FS), prediction, and parameter optimization. In addition, MOGOA-based FS technique is utilized in the selection of optimum subset of features. Besides, DELM-based predictive model is also applied in forecasting the load requirements. The proposed MOGOA model is also applied in FS and the selection of optimal DELM parameters to improve the predictive outcome. To inspect the effectual outcome of the proposed MOGOA-DELM model, a series of simulations was performed using UK Smart Meter dataset. In the experimentation procedure, the proposed model achieved the highest accuracy of 85.80% and the results established the superiority of the proposed model in predicting the testing data. Tech Science Press 2022 Article PeerReviewed Varghese, Lijo Jacob and Dhayalini, K. and Jacob, Suma Sira and Ali, Ihsan and Abdelmaboud, Abdelzahir and Eisa, Taiseer Abdalla Elfadil (2022) Optimal load forecasting model for Peer-to-Peer energy trading in smart grids. CMC-Computers Materials & Continua, 70 (1). pp. 1053-1067. ISSN 1546-2218, DOI https://doi.org/10.32604/cmc.2022.019435 <https://doi.org/10.32604/cmc.2022.019435>. 10.32604/cmc.2022.019435
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Varghese, Lijo Jacob Dhayalini, K. Jacob, Suma Sira Ali, Ihsan Abdelmaboud, Abdelzahir Eisa, Taiseer Abdalla Elfadil Optimal load forecasting model for Peer-to-Peer energy trading in smart grids
description	Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm (MOGOA) with Deep Extreme Learning Machine (DELM)-based short-term load predictive technique i.e., MOGOA-DELM model for P2P Energy Trading (ET) in SGs. The proposed MOGOA-DELM model involves four distinct stages of operations namely, data cleaning, Feature Selection (FS), prediction, and parameter optimization. In addition, MOGOA-based FS technique is utilized in the selection of optimum subset of features. Besides, DELM-based predictive model is also applied in forecasting the load requirements. The proposed MOGOA model is also applied in FS and the selection of optimal DELM parameters to improve the predictive outcome. To inspect the effectual outcome of the proposed MOGOA-DELM model, a series of simulations was performed using UK Smart Meter dataset. In the experimentation procedure, the proposed model achieved the highest accuracy of 85.80% and the results established the superiority of the proposed model in predicting the testing data.
format	Article
author	Varghese, Lijo Jacob Dhayalini, K. Jacob, Suma Sira Ali, Ihsan Abdelmaboud, Abdelzahir Eisa, Taiseer Abdalla Elfadil
author_facet	Varghese, Lijo Jacob Dhayalini, K. Jacob, Suma Sira Ali, Ihsan Abdelmaboud, Abdelzahir Eisa, Taiseer Abdalla Elfadil
author_sort	Varghese, Lijo Jacob
title	Optimal load forecasting model for Peer-to-Peer energy trading in smart grids
title_short	Optimal load forecasting model for Peer-to-Peer energy trading in smart grids
title_full	Optimal load forecasting model for Peer-to-Peer energy trading in smart grids
title_fullStr	Optimal load forecasting model for Peer-to-Peer energy trading in smart grids
title_full_unstemmed	Optimal load forecasting model for Peer-to-Peer energy trading in smart grids
title_sort	optimal load forecasting model for peer-to-peer energy trading in smart grids
publisher	Tech Science Press
publishDate	2022
url	http://eprints.um.edu.my/33611/
_version_	1740826047674318848
score	13.211869

Optimal load forecasting model for Peer-to-Peer energy trading in smart grids

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