Open Access

ARTICLE

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

by Lijo Jacob Varghese¹, K. Dhayalini², Suma Sira Jacob³, Ihsan Ali^4,*, Abdelzahir Abdelmaboud⁵, Taiseer Abdalla Elfadil Eisa⁶

1 Department of Electrical and Electronics Engineering, Christian College of Engineering and Technology, Oddanchatram, 624619, India
2 Department of Electrical and Electronics Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
3 Department of Computer Science and Engineering, Christian College of Engineering and Technology, Oddanchatram, 624619, India
4 Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia
5 Department of Information Systems, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
6 Department of Information Systems - Girls Section, King Khalid University, Mahayil, 62529, Saudi Arabia

* Corresponding Author: Ihsan Ali. Email:

Computers, Materials & Continua 2022, 70(1), 1053-1067. https://doi.org/10.32604/cmc.2022.019435

Received 13 April 2021; Accepted 28 May 2021; Issue published 07 September 2021

Abstract

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.

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