Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture. (25th May 2022)

• Record Type: Journal Article
• Authors: Refaai, Mohamad Reda A.; Vonteddu, Shanmukha Naga Raju; Nunna, Prasanthi Kumari; Kumar, P. Suresh; Anbu, C.; Markos, Mebratu
• Other Names: Mohanavel V. Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2022(2022)
• Publication Date: 2022-05-25
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2022/6844853
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need.