Optimization Techniques for Home Energy Management Systems
A Comprehensive Review, Critical Analysis, and Future Directions
Datos Bibliográficos
| ID | 19489157 |
|---|---|
| Autores | Md Mamun Ur Rashid (0009-0003-7190-1076, Federation University), Jiefeng Hu (0000-0001-6725-4564, Federation University), Md Alamgir Hossain (0000-0002-7368-6424, University of Southern Queensland), Nima Amjady (0000-0003-1308-1738, Federation University), Syed Islam (Federation University) |
| Año | 2026 |
| Volumen | 10 |
| Número | 6 |
| Páginas | 324 |
| Fecha de publicación | 2026-06-10 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Urban Science (JOURNAL) |
| Identificadores de la revista | ISSN: 2413-8851 • E-ISSN: 2413-8851 |
| Editorial | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/urbansci10060324 |
| OpenAlex | W7164121125 |
| Idioma | EN |
| Referencias citadas | 77 |
The increasing integration of renewable energy sources, smart appliances, and distributed energy technologies has significantly increased the complexity of residential energy systems, necessitating advanced Home Energy Management Systems (HEMS). Optimization techniques play a critical role in achieving key objectives, including energy cost reduction, load balancing, minimizing the peak-to-average ratio, and enhancing user comfort. This paper presents a comprehensive review and critical analysis of optimization techniques employed in HEMS, including mathematical methods, metaheuristic algorithms, artificial intelligence (AI)-based approaches, and rule-based strategies. These techniques are systematically classified and compared based on scalability, computational complexity, uncertainty handling, and real-time applicability. The analysis reveals that while conventional methods provide reliable solutions for structured problems, AI-based techniques offer superior adaptability and performance in dynamic and data-driven environments. Furthermore, key research gaps are identified, including limited multi-objective optimization, inadequate consideration of uncertainty and electric vehicle integration, and the lack of real-world implementation. Finally, future research directions are outlined, emphasizing hybrid optimization frameworks and intelligent, IoT-enabled energy management systems
Adaptability · Energy management · Energy management system · Metaheuristic · Renewable energy · Building Energy and Comfort Optimization · Integrated Energy Systems Optimization · Smart Grid Energy Management
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |