IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v19y2024ip628-641..html
   My bibliography  Save this article

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.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctad144
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Bass, Brett & New, Joshua, 2023. "How will United States commercial building energy use be impacted by IPCC climate scenarios?," Energy, Elsevier, vol. 263(PE).
    3. 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).
    4. 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).
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shaik, Saleem, 2024. "Contribution of climate change to sector-source energy demand," Energy, Elsevier, vol. 294(C).
    2. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
    3. Cui, Ying & Yan, Da & Hong, Tianzhen & Xiao, Chan & Luo, Xuan & Zhang, Qi, 2017. "Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China," Applied Energy, Elsevier, vol. 195(C), pages 890-904.
    4. Merlin Keller & Guillaume Damblin & Alberto Pasanisi & Mathieu Schumann & Pierre Barbillon & Fabrizio Ruggeri, 2022. "Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing," Post-Print hal-04071903, HAL.
    5. Cheng, Qian & Liu, Pan & Xia, Qian & Cheng, Lei & Ming, Bo & Zhang, Wei & Xu, Weifeng & Zheng, Yalian & Han, Dongyang & Xia, Jun, 2023. "An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change," Energy, Elsevier, vol. 278(C).
    6. Guan, Zepeng & Hossain, Mohammad Razib & Sheikh, Muhammad Ramzan & Khan, Zeeshan & Gu, Xiao, 2023. "Unveiling the interconnectedness between energy-related GHGs and pro-environmental energy technology: Lessons from G-7 economies with MMQR approach," Energy, Elsevier, vol. 281(C).
    7. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
    8. Francesco Fiorito & Giandomenico Vurro & Francesco Carlucci & Ludovica Maria Campagna & Mariella De Fino & Salvatore Carlucci & Fabio Fatiguso, 2022. "Adaptation of Users to Future Climate Conditions in Naturally Ventilated Historic Buildings: Effects on Indoor Comfort," Energies, MDPI, vol. 15(14), pages 1-21, July.
    9. Liu, Yanli & Wang, Junyi & Liu, Liqi, 2024. "Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events," Applied Energy, Elsevier, vol. 376(PA).
    10. Kočí, Jan & Kočí, Václav & Maděra, Jiří & Černý, Robert, 2019. "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 22-32.
    11. De Masi, Rosa Francesca & Gigante, Antonio & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2021. "Impact of weather data and climate change projections in the refurbishment design of residential buildings in cooling dominated climate," Applied Energy, Elsevier, vol. 303(C).
    12. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    13. Meihang Zhang & Hua Zhang & Wei Yan & Zhigang Jiang & Shuo Zhu, 2023. "An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    14. Dasaraden Mauree & Silvia Coccolo & Amarasinghage Tharindu Dasun Perera & Vahid Nik & Jean-Louis Scartezzini & Emanuele Naboni, 2018. "A New Framework to Evaluate Urban Design Using Urban Microclimatic Modeling in Future Climatic Conditions," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    15. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    16. Yassaghi, Hamed & Gurian, Patrick L. & Hoque, Simi, 2020. "Propagating downscaled future weather file uncertainties into building energy use," Applied Energy, Elsevier, vol. 278(C).
    17. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
    18. Luka Pajek & Mitja Košir, 2021. "Exploring Climate-Change Impacts on Energy Efficiency and Overheating Vulnerability of Bioclimatic Residential Buildings under Central European Climate," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    19. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
    20. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:ijlctc:v:19:y:2024:i::p:628-641.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.