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From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

Author

Listed:
  • Yanyan Huang

    (School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

  • Yi Yang

    (School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

  • Hangyi Ren

    (School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

  • Lanxin Ye

    (School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

  • Qinhan Liu

    (School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

Abstract

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

Suggested Citation

  • Yanyan Huang & Yi Yang & Hangyi Ren & Lanxin Ye & Qinhan Liu, 2024. "From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance," Sustainability, MDPI, vol. 16(16), pages 1-27, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7193-:d:1461060
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    References listed on IDEAS

    as
    1. Zhang, Ji & Xu, Le & Shabunko, Veronika & Tay, Stephen En Rong & Sun, Huixuan & Lau, Stephen Siu Yu & Reindl, Thomas, 2019. "Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city," Applied Energy, Elsevier, vol. 240(C), pages 513-533.
    2. Rui Xiao & Xiaoyu Yu & Zhonghao Zhang & Xue Wang, 2021. "Built‐up land expansion simulation with combination of naive Bayes and cellular automaton model—A case study of the Shanghai‐Hangzhou Bay agglomeration," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1804-1825, September.
    3. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    4. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    5. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
    6. Ron Johnston & Kelvyn Jones & David Manley, 2018. "Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1957-1976, July.
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