ANN-Based Grid-Connected Solar PV Integrated Smart Home Energy Management System

With the growing scarcity of fossil fuels and frequent economic crises, renewable energy sources (RES) and artificial intelligence (AI) techniques present a groundbreaking combination. AI is increasingly popular in energy research due to its accuracy. In this paper, an artificial neural network (ANN)-specifically a Levenberg-Marquardt (LM)-based smart home energy management system (SHEMS)-is developed and integrated with a grid-connected solar photovoltaic (PV) system. The LM algorithm shows the regression value 1, which is implemented with 1,000 sample data taken from a solar PV park. Seventy percent of the data is used to train the LM algorithm, while fifteen percent is to test and the rest to validate. The proposed system connects utility companies and consumers, allowing for bi-directional communication, which improves the efficiency of a sustainable home energy management system.