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

• 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/
• Institution: Universiti Malaya

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.