A Rule-based Expert System for Home Power Management Incorporating Real-Life Data Sets

Abstract

Photovoltaic (PV) and electric vehicle (EV) systems are gaining traction as a result of increased energy demands and the global imperative to provide affordable and sustainable energy. A small-scale home area power network (HAPN) is explored in this article, which integrates an intelligent energy management system (iEMS) using a cost-effective power scheduling approach. The purpose of this paper is to examine the proposed iEMS capabilities using real-world yearly data sets on residential energy consumption, electric vehicle driving trends, and electric vehicle battery (dis)charging patterns. Additionally, by integrating a battery life-cycle degradation model, a percentage of EV storage capacity loss is calculated. The comfort of consumers is ensured by matching their energy demands to the least expensive energy supplies. The simulation results illustrate the proposed iEMS behavior utilizing a variety of performance measures, and the ideal scheduling signals for a mix of energy sources are thus presented.