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

Yanyan Huang (), Yi Yang, Hangyi Ren, Lanxin Ye and Qinhan Liu
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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

Sustainability, 2024, vol. 16, issue 16, 1-27

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.

Keywords: photovoltaic potential; block form; LightGBM; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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