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Abstract

Buildings contribute to 40% of energy consumption and 30% of CO2 emissions in 2019 globally, therefore it is necessary to exploit different solutions to decrease the corresponding energy demand, including green and cool roofs as well as on-site energy generation. To evaluate the potential of such technologies, one major input data for models and calculations is the available roof area, yet the literature shows a huge knowledge gap in this regard. Therefore, this paper contributes to filling this gap by estimating the roof availability over the period 2022–2060, using the detailed regional projections of the BISE (Building Integrated Solar Energy) model. Our results show that the roof area is likely to increase globally and in most of the analysed regions over the forthcoming decades, driven primarily by newly built tertiary buildings. In European context, the future increase of commercial/public rooftops is projected to be more pronounced for the western countries, although the overall growth is predicted to be slightly offset by shrinking residential rooftops both in the western and eastern regions. This study also demonstrates that despite the shading-related uncertainties of the estimation, the dimension of the available rooftop area could ensure significant potential for energy production and thermal regulation.

Publication Date

2025-01-01

Publication Title

Developments in the Built Environment

Volume

21

Acceptance Date

2025-01-14

Deposit Date

2025-02-17

Funding

Combining GIS with machine learning techniques tends to be the latest path in deriving highly reliable rooftop data for different building-related analyses. A remarkable strength of this approach is that it ensures the veracious recognition and segmentation of such complex shapes and geometries as building surfaces (Ren et al., 2022). This statement is especially valid when the rooftop classification comes to very detailed Light Detection and Ranging (LiDAR) point clouds. LiDAR data is often used for creating so-called digital surface models (DSMs) based on which the 3-D model of different surface elements (e.g., trees, bridges, buildings) can be constructed. Then the 3-D model of buildings can be useful to separate useful and useless rooftop areas as well as their orientation, slope and shading (Gassar and Cha, 2021). LiDAR data and/or machine learning techniques (e.g., XGboost \u2013 XG; Random Forest \u2013 RF; Extreme Learning Machine Ensembles \u2013 ELM-E; Support Vector Regression \u2013 SVR; Deep Convolutional Neural Network \u2013 DCNN) have been successfully applied by many investigations in the literature to estimate the location and characteristics of building rooftops (SVR: (Assouline et al., 2017); XG: (Joshi et al., 2021); RF + ELM-E: (Walch et al., 2020); DCNN (Ren et al., 2022):).Despite all of the limitations of our study, the solar and green roof area estimations can offer reasonable reference for many theoretical and practical studies. For instance, the roof area tendencies could be the basis of regional solar (PV, thermal and hybrid) and wind potential estimations. In the EU, as a part of the Solar Rooftop Initiative (European Commission, 2022) issued in May 2022, significant fraction of the European building rooftops is expected to be covered by solar PV over the next decade to support the energy transition towards 2050. To address the effects of upscaling the current level of rooftop PV installations on the growth of clean electricity production, new investigations could benefit from our data, for example, in constructing different scenarios and sensitivity analyses, linking multiple utilization rates and building types.The authors declare the following financial interests/personal relationships which may be considered as potential competing interestsDiana Urge-Vorsatz, Souran Chatterjee, Gergely Molnar reports financial support was provided by European Commission. Diana Urge-Vorsatz, Souran Chatterjee reports financial support was provided by Japan Ministry of Economy Trade and Industry. Luisa F. Cabeza reports financial support was provided by Catalan Government.This paper is based on research conducted within the EC funded Horizon 2020 Framework Programme for Research and Innovation (EU H2020) Project titled \u201CSustainable Energy Transitions Laboratory\u201D (SENTINEL)- Grant Agreement No. 837089. Dr. \u00DCrge-Vorsatz, Dr. Chatterjee and Dr. Moln\u00E1r would like to acknowledge the support from the EC. The content of the paper is the sole responsibility of its authors and does not necessarily reflect the views of the EC. Dr. \u00DCrge-Vorsatz and Dr. Chatterjee also received funding from the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is part of the initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and International Institute for Applied Systems Analysis (IIASA) (and funded by Ministry of Economy, Trade, and Industry (METI), Japan). Dr. Cabeza would like to thank the Catalan Government for the quality accreditation given to her research group (2021 SGR 01615). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme. This paper is based on research conducted within the EC funded Horizon 2020 Framework Programme for Research and Innovation (EU H2020) Project titled \u201CSustainable Energy Transitions Laboratory\u201D (SENTINEL)- Grant Agreement No. 837089. Dr. \u00DCrge-Vorsatz, Dr. Chatterjee and Dr. Moln\u00E1r would like to acknowledge the support from the EC. The content of the paper is the sole responsibility of its authors and does not necessarily reflect the views of the EC. Dr. \u00DCrge-Vorsatz and Dr. Chatterjee also received funding from the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is part of the initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and International Institute for Applied Systems Analysis (IIASA) (and funded by Ministry of Economy, Trade, and Industry (METI), Japan).

Keywords

BISE model, Building integrated solar energy, Green roofs, Roof area projection

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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