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A novel acceleration approach to shadow calculation based on sunlight channel for urban building energy modeling.
- Source :
-
Energy & Buildings . Jul2024, Vol. 315, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Shadow effect among buildings has great impacts on the building energy consumption and the performance of building photovoltaic, and calculating shadows on building surfaces in urban building energy modeling (UBEM) faces challenges of inefficiency, especially for dense urban areas. In this study, a novel shadow calculation approach based on sunlight channel is proposed that can streamline the surrounding environment and accelerate the shadow calculation process. The sunlight-channel algorithm can further accelerate the shadow calculation process by dynamically predetermining the shading surfaces according to the actual solar position. In a real urban context, the proposed approach can accelerate the computation process by over 10 times over the baseline and over 34 times over the non-accelerated method, with a mean absolute percentage error (MAPE) of 1.13% for the total solar radiation. The proposed approach copes well with both large-scale urban models and the complexity of building structures, particularly for urban models with complex changes in building heights. This approach can significantly enhance the computational efficiency in complex urban environments, facilitating an accurate and rapid analysis of the energy consumption and solar potential of buildings in dense cities. • Sunlight channel algorithm was proposed to screen the shading surfaces dynamically. • A rapid method for shadow calculation for complex urban environments was proposed. • 13 neighborhoods with different spatial scale and height variation were compared. • Accelerate by over 34 times over the non-accelerated method, with 98.8% accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03787788
- Volume :
- 315
- Database :
- Academic Search Index
- Journal :
- Energy & Buildings
- Publication Type :
- Academic Journal
- Accession number :
- 177602276
- Full Text :
- https://doi.org/10.1016/j.enbuild.2024.114244