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

Author

Listed:
  • Yukai Zou
  • Zhuoxi Chen
  • Jialiang Guo
  • Yingsheng Zheng
  • Xiaolin Yang

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.

Suggested Citation

  • Yukai Zou & Zhuoxi Chen & Jialiang Guo & Yingsheng Zheng & Xiaolin Yang, 2024. "A data-driven framework for fast building energy demand estimation across future climate conditions," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 19, pages 628-641.
    • Handle: RePEc:oup:ijlctc:v:19:y:2024:i::p:628-641.
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    References listed on IDEAS

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    1. Baglivo, Cristina & Congedo, Paolo Maria & Murrone, Graziano & Lezzi, Dalila, 2022. "Long-term predictive energy analysis of a high-performance building in a mediterranean climate under climate change," Energy, Elsevier, vol. 238(PA).
    2. Fonseca, Jimeno A. & Nevat, Ido & Peters, Gareth W., 2020. "Quantifying the uncertain effects of climate change on building energy consumption across the United States," Applied Energy, Elsevier, vol. 277(C).
    3. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    4. Nik, Vahid M., 2016. "Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)," Applied Energy, Elsevier, vol. 177(C), pages 204-226.
    5. Bass, Brett & New, Joshua, 2023. "How will United States commercial building energy use be impacted by IPCC climate scenarios?," Energy, Elsevier, vol. 263(PE).
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