Semantic obstruction detection for improved solar energy potential modeling in urban areas

Achieving precise and scalable solar potential estimation in urban settings is challenging due to the presence of a wide variety of obstructions. To address this issue, we developed a novel urban solar potential modeling method based on an improved 2-phase daylight model. Utilizing a dynamic graph c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Building simulation Jg. 18; H. 4; S. 791 - 827
Hauptverfasser: Tian, Bowen, Loonen, Roel C. G. M., Hensen, Jan L. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beijing Tsinghua University Press 01.04.2025
Springer Nature B.V
Schlagworte:
Alternative energy sources
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
Building Construction and Design
Engineering
Engineering Thermodynamics
Graph neural networks
Heat and Mass Transfer
Image segmentation
Irradiation
Modelling
Monitoring/Environmental Analysis
Obstructions
Photovoltaic cells
Renewable energy
Renewable resources
Research Article
Semantic segmentation
Semantics
Shading
Solar energy
Solar radiation
Sustainable development
Urban areas
Urban development
Urban environments
solar obstruction
shading impacts
urban solar irradiation
dynamic graph CNN
ISSN:1996-3599, 1996-8744
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Achieving precise and scalable solar potential estimation in urban settings is challenging due to the presence of a wide variety of obstructions. To address this issue, we developed a novel urban solar potential modeling method based on an improved 2-phase daylight model. Utilizing a dynamic graph convolutional neural network semantic segmentation model to process urban point cloud data, our method distinguishes between different types of solar obstructions, assigning specific simulation hyperparameters accordingly. Demonstrated through experiments, our method significantly outperforms traditional models by avoiding the underestimation of shading impacts—by up to 60% for monthly solar irradiation potential and 40% for annual PV yield potential. Moreover, our method accurately accounts for complex solar transmission through tree canopies, avoiding underestimation of PV energy potential by up to 7% compared to its predecessor (Pyrano 1.0). These improvements offer substantial benefits for managing PV shading risks, configuring PV systems, and managing renewable resources, especially in urban areas with complex geometries and dynamically changing shading conditions. Our findings underscore the method’s potential to enhance decision-making in sustainable urban development and renewable energy integration.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-3599
1996-8744
DOI:10.1007/s12273-025-1238-6