Recent optimization methods and techniques in residential home energy management systems

Residential Home Energy Management System (HEMS) has emerged as a key player in promoting energy efficiency, cost savings, and environmental sustainability within modern households. HEMS provides a crucial solution that combines smart technology and sustainable living, enabling homeowners to monitor, control, and optimize energy consumption. The architecture of HEMS is complex, incorporating sensors, actuators, communication networks, central controllers, user interfaces, and cloud services, and they can integrate renewable energy sources to enhance performance. Optimization techniques in HEMS range from exact mathematical methods, such as linear programming, to approximate approaches including meta-heuristics and Artificial Intelligence (AI), providing a dynamic framework for improving energy usage. This paper presents a comprehensive up-to-date review of HEMS research, focusing on optimization objective functions, methodologies, and techniques. Strengths and limitations of recent studies are critically analyzed, and potential directions for future research are proposed to assist researchers in advancing the field of intelligent residential energy management. Building on this foundation, this paper proposes a new conceptual Green Smart Home Energy Management System (GSHEMS), a novel framework designed to further enhance residential energy management. GSHEMS integrates renewable generation, intelligent storage management, predictive weather-based load scheduling, and dimming-based appliance control within a unified architecture. While GSHEMS is at the conceptual phase, its novelty lies in the holistic integration of AI-based optimization, predictive analytics, dual operation modes (automatic and manual), and real-time visualization, which is expected to outperform conventional HEMS in maximizing renewable utilization, reducing grid dependency, and maintaining user comfort with generating tailored recommendations for energy saving, based on forecasted weather and predicted renewable availability, transforming conventional home into Green Smart Home (GSH) or nearly Zero Energy Smart Home (ZESH).