Simulation of solar radiation on metropolitan building surfaces

A novel and flexible research framework

Datos Bibliográficos

ID21228366
AutoresPingan 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, autor de correspondencia), 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
Año2023
Volumen93
Páginas104469
Fecha de publicación2023-06-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSustainable Cities and Society (JOURNAL)
Identificadores de la revistaISSN: 2210-6707 • E-ISSN: 2210-6715
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2023.104469
OpenAlexW4323852631
IdiomaEN
Citas recibidas7
Referencias citadas52

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