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Archetype building energy modeling approaches and applications: A review

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  • Shen, Pengyuan
  • Wang, Huilong

Abstract

Archetype Building Energy Models (ABEMs) are representations of buildings in a certain region that enable the assessment of energy use across building stocks in a bottom-up manner, playing an important role in building energy policy making, energy efficiency measure evaluation, and sustainable urban planning. However, the selection of the suitable modeling approach can be up to various factors due to the diversity of available methods and the specific requirements of application scenarios. This review aims to address this issue by analyzing and comparing different approaches in ABEM, namely the building codes-based approach, data-driven approach, and hybrid approach, and present the strengths, weaknesses, and real-world applicability of the modeling approaches. The fitness of each method to different research purposes and contexts is explored. This study sheds light on key factors influencing the choice of the ABEM method, including the objectives of the research, available data quality, computational resources, and model accuracy. By gathering and synthesizing available information from the state of art studies, an overview and guideline for researchers and decision-makers who intend to leverage ABEM for various purposes is provided. It not only helps in the better understanding of existing modeling methods but also identifies challenges faced with ABEMs and the potential in future improvement. This work also identifies opportunities in future ABEM and its flexibility in various application scenarios, and it is anticipated that ABEMs will continue to play important roles in informing engineering design, influencing regulations, optimizing energy systems, guiding policy decisions for sustainable building development.

Suggested Citation

  • Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:rensus:v:199:y:2024:i:c:s1364032124002016
    DOI: 10.1016/j.rser.2024.114478
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