Abstract—The increasing integration of renewable energy sources and the electrification of building sectors have created significant operational challenges for modern power systems, particularly at the distribution level. Buildings equipped with flexible assets, such as electric vehicles, heat pumps, smart appliances, HVAC and thermal mass, offer significant opportunities to enhance grid flexibility through demand response. This paper provides a comprehensive state-of-the-art review of multi-method approaches for quantifying and leveraging building energy flexibility, with a specific emphasis on optimization-based, simulation-based, data-driven/Artificial Intelligence (AI), and rule-based approaches. For each category, the characteristics, strengths, and limitations are summarised. The review highlights how each method can model or predict flexibility under varying conditions and underscores the necessity of tailoring choices to specific applications. An evaluation framework that integrates multiple methods and emphasises human-centric control is discussed. The findings reveal emerging trends, including the significance of explainable artificial intelligence and the real-world validation of flexibility schemes. Research gaps are identified, and future directions are suggested, such as integrating occupant behavior models and deploying hybrid methods in smart grids. These insights offer guidance for researchers and practitioners seeking to advance building energy flexibility.

Keywords—building energy flexibility, data-driven/Artificial Intelligence (AI) approaches, optimization-based methods, simulation-based methods, rule-based methods

Cite: Md Shamsul Arefin Tanim, Md Asif Abbas, "A State-of-the-art Review of Multi-method Approaches for Building Energy Flexibility Evaluation," International Journal of Smart Grid and Clean Energy, Vol. 15, No. 2, pp. 79-91, 2026. doi: 10.12720/sgce.15.2.79-91

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).