A data-driven framework for fast building energy demand estimation across future climate conditions

Yukai Zou, Zhuoxi Chen, Jialiang Guo, Yingsheng Zheng and Xiaolin Yang

International Journal of Low-Carbon Technologies, 2024, vol. 19, 628-641

Abstract: The rapid and precise forecasting of building energy requirements plays a crucial role in decision-making processes for architects during the early design phase. This study introduces a data-driven framework capable of projecting energy demands in the context of evolving climate conditions. We evaluated four widely-used machine learning algorithms. Our results indicated that a hybrid approach, integrating Catboost and Bayesian optimization, excelled in both accuracy and efficiency for predicting building energy demand under climate change conditions. The framework proposed herein has potential applications in fostering sustainability in early-stage architectural design.

Keywords: climate change; future energy demand; data-driven model; parametric building simulation; Bayesian optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:19:y:2024:i::p:628-641.

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