Framework for Mapping and Optimizing the Solar Rooftop Potential of Buildings in Urban Systems

Nima Narjabadifam, Mohammed Al-Saffar, Yongquan Zhang, Joseph Nofech, Asdrubal Cheng Cen, Hadia Awad, Michael Versteege and Mustafa Gül
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Nima Narjabadifam: Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Mohammed Al-Saffar: Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Yongquan Zhang: Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Joseph Nofech: Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Asdrubal Cheng Cen: Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Hadia Awad: National Research Council Canada, Ottawa, ON K1V 1J8, Canada
Michael Versteege: Energy Management & Sustainable Operations, University of Alberta, Edmonton, AB T6G 1H9, Canada
Mustafa Gül: Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Energies, 2022, vol. 15, issue 5, 1-32

Abstract: The accurate prediction of the solar energy that can be generated using the rooftops of buildings is an essential tool for many researchers, decision makers, and investors for creating sustainable cities and societies. This study is focused on the development of an automated method to extract the useable areas of rooftops and optimize the solar PV panel layout based on the given electricity loading of a building. In this context, the authors of this article developed two crucial methods. First, a special pixel-based rooftop recognition methodology was developed to analyze detailed and complex rooftop types while avoiding the challenges associated with the nature of the particular building rooftops. Second, a multi-objective enveloped min–max optimization algorithm was developed to maximize solar energy generation and minimize energy cost in terms of payback based on the marginal price signals. This optimization algorithm facilitates the optimal integration of three controlled variables—tilt angle, azimuth angle, and inter-row spacing—under a non-linear optimization space. The performance of proposed algorithms is demonstrated using three campus buildings at the University of Alberta, Edmonton, Alberta, Canada as case studies. It is shown that the proposed algorithms can be used to optimize PV panel distribution while effectively maintaining system constraints.

Keywords: roof identification; roof classification; computer vision; mapping; optimizing; solar rooftop potential; buildings; urban systems; photovoltaics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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