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• New automated method for modeling 2D rooftops from open-source satellite images. • Cheaper and faster processing compared with LiDAR and other traditional methods. • Capability of achieving large-scale operations in the city-scale. • Urban solar energy assessment provides basis of district energy planning. This study presents a novel approach to detect the city-wide solar potential which utilizes image segmentation with deep learning technology unlike traditional methods. In order to study the solar energy potential in the urban scale, there exists a requirement to quantify the roof area of buildings which are available to receive solar radiation, calculate the total solar radiation obtained within the region based on the meteorological conditions, and determine the total solar energy potential with carbon emissions savings and the economic recovery period. However, obtaining the overall roof area of a city is an existing difficulty when considering the quantification of solar potential in the urban scale. This study utilizes the U-Net of deep learning technology, and a large range of satellite maps to identify the building roof, in order to estimate the city's solar potential. This research established that the urban roofs of Wuhan have an annual photovoltaic electricity generation potential of 17292.30 × 106 kWh/year. [ABSTRACT FROM AUTHOR]

Rapid and uncontrolled urbanization is continuously increasing buildings’ energy consumption and greenhouse gas emissions into the atmosphere. In this scenario, solar energy integrated into the built environment can play an important role in optimizing the use of renewable energy sources on urban surfaces. Preliminary solar analyses to map the solar accessibility and solar potential of building surfaces (roofs and façades) should become a common practice among urban planners, architects, and public authorities. This paper presents an approach to support urban actors to assess solar energy potential at the neighborhood scale and to address the use of solar energy by considering overshadowing effects and solar inter-building reflections in accordance with urban morphology and building characteristics. The approach starts with urban analysis and solar irradiation analysis to elaborate solar mapping of façades and roofs. Data processing allows assessment of the solar potential of the whole case study neighborhood of Sluppen in Trondheim (Norway) by localizing the most radiated parts of buildings’ surfaces. Reduction factors defined by a new method are used to estimate the final solar potential considering shadowing caused by the presence of buildings’ architectural elements (e.g., glazed surfaces, balconies, external staircases, projections) and self-shading. Finally, rough estimation of solar energy generation is assessed by providing preliminary recommendations for solar photovoltaic (PV) systems suited to local conditions. Results show that depending on urban morphology and buildings’ shapes, PV systems can cover more than 40% of the total buildings’ energy needs in Trondheim.

Rapid and uncontrolled urbanization is continuously increasing buildings' energy consumption and greenhouse gas emissions into the atmosphere. In this scenario, solar energy integrated into the built environment can play an important role in optimizing the use of renewable energy sources on urban surfaces. Preliminary solar analyses to map the solar accessibility and solar potential of building surfaces (roofs and façades) should become a common practice among urban planners, architects, and public authorities. This paper presents an approach to support urban actors to assess solar energy potential at the neighborhood scale and to address the use of solar energy by considering overshadowing effects and solar inter-building reflections in accordance with urban morphology and building characteristics. The approach starts with urban analysis and solar irradiation analysis to elaborate solar mapping of façades and roofs. Data processing allows assessment of the solar potential of the whole case study neighborhood of Sluppen in Trondheim (Norway) by localizing the most radiated parts of buildings' surfaces. Reduction factors defined by a new method are used to estimate the final solar potential considering shadowing caused by the presence of buildings' architectural elements (e.g., glazed surfaces, balconies, external staircases, projections) and self-shading. Finally, rough estimation of solar energy generation is assessed by providing preliminary recommendations for solar photovoltaic (PV) systems suited to local conditions. Results show that depending on urban morphology and buildings' shapes, PV systems can cover more than 40% of the total buildings' energy needs in Trondheim. [ABSTRACT FROM AUTHOR]