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Multi-objective optimization for energy-efficient building design considering urban heat island effects

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  • Zhang, Yan
  • Teoh, Bak Koon
  • Zhang, Limao

Abstract

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

Suggested Citation

  • Zhang, Yan & Teoh, Bak Koon & Zhang, Limao, 2024. "Multi-objective optimization for energy-efficient building design considering urban heat island effects," Applied Energy, Elsevier, vol. 376(PA).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s0306261924015009
    DOI: 10.1016/j.apenergy.2024.124117
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    References listed on IDEAS

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    1. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    2. Guo, Jing & Lin, Penghui & Zhang, Limao & Pan, Yue & Xiao, Zhonghua, 2023. "Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption," Applied Energy, Elsevier, vol. 350(C).
    3. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    4. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    5. 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.
    6. Mangkuto, Rizki A. & Rohmah, Mardliyahtur & Asri, Anindya Dian, 2016. "Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics," Applied Energy, Elsevier, vol. 164(C), pages 211-219.
    7. Li, Xiaoma & Zhou, Yuyu & Yu, Sha & Jia, Gensuo & Li, Huidong & Li, Wenliang, 2019. "Urban heat island impacts on building energy consumption: A review of approaches and findings," Energy, Elsevier, vol. 174(C), pages 407-419.
    8. Zhang, Yan & Teoh, Bak Koon & Zhang, Limao, 2023. "Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method," Energy, Elsevier, vol. 269(C).
    9. Kheiri, Farshad, 2018. "A review on optimization methods applied in energy-efficient building geometry and envelope design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 897-920.
    10. Pilechiha, Peiman & Mahdavinejad, Mohammadjavad & Pour Rahimian, Farzad & Carnemolla, Phillippa & Seyedzadeh, Saleh, 2020. "Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency," Applied Energy, Elsevier, vol. 261(C).
    11. Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
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    1. Sergiusz Pimenow & Olena Pimenowa & Piotr Prus, 2024. "Challenges of Artificial Intelligence Development in the Context of Energy Consumption and Impact on Climate Change," Energies, MDPI, vol. 17(23), pages 1-34, November.
    2. Fei Guo & Shiyu Miao & Sheng Xu & Mingxuan Luo & Jing Dong & Hongchi Zhang, 2024. "Multi-Objective Optimization Design for Cold-Region Office Buildings Balancing Outdoor Thermal Comfort and Building Energy Consumption," Energies, MDPI, vol. 18(1), pages 1-21, December.

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