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Simulation of solar radiation on metropolitan building surfaces

A novel and flexible research framework

Bibliographic Data

ID21228366
AuthorsPingan Ni (0000-0001-8290-4203, Xi'an University of Architecture and Technology), Zengfeng Yan (0000-0001-7323-4922, Xi'an University of Architecture and Technology, corresponding author), Yingjun Yue (Xi'an University of Architecture and Technology), Liangliang Xian (Xi'an University of Architecture and Technology), Fuming Lei (0000-0002-6257-0493, Xi'an University of Architecture and Technology), Yan Xia (0000-0002-4648-2012, Xi'an University of Architecture and Technology), Xia Yan
Year2023
Volume93
Pages104469
Publication date2023-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSustainable Cities and Society (JOURNAL)
Journal identifiersISSN: 2210-6707 • E-ISSN: 2210-6715
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2023.104469
OpenAlexW4323852631
LanguageEN
Citations received7
References cited52

The current use of the Geographic Information System (GIS) and Parametric Environmental Simulation (PES) to calculate the solar radiation on building surfaces cannot be effectively applied to large-scale scenes. To overcome the scale and dimensionality issues when estimating solar radiation on large-scale building surfaces, an ingenious solar radiation simulation framework, which can extend the depth of study and expand the number of possible calculations, is proposed. First, the framework is used to investigate the performance and error of different clustering methods used for urban splitting. K-means displays the best performance, and the MAPE is 0.89%. In addition, Monte Carlo integration (MCI) is applied to explore a microelement reconstruction method for the complex contours of building roofs. NSGA-II is used to derive the optimal parameters and reconstruction accuracy for the reconstruction process. Notably, R 2 reaches 0.98, and the MAPE is only 0.25%. The framework is applied to evaluate the radiation type, spatial and temporal distributions, and the overall occlusion rate ( RT ) of clusters of over 600,000 buildings in Shanghai. The results indicate that approximately 80% of the samples in Shanghai display an RT in a mid-high to high range, and less than 3.5% of the samples display an low RT that is low. Based on this study, we can refine assessments of solar radiation on the surfaces of buildings at the city scale and even the national scale and thus provide a reference for the utilization of solar energy resources in areas where solar resources are not abundant

Artificial neural network · Cartography · Cluster analysis · Data mining · Electrical engineering · Geography · Mean absolute percentage error · Monte Carlo method · Optics · Parametric statistics · Physics · Simulation · Solar energy · Statistics · Building Energy and Comfort Optimization · Computer Science · Engineering · Environmental Science · Mathematics · Solar Radiation and Photovoltaics · Urban Heat Island Mitigation · Artificial Intelligence · Radiation

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Unique citing works7
Citations per year2,33
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 7
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