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Solar resource assessment of modern parking machines in an urban environment

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  • Rehman, Naveed ur
  • Katebi, Milad
  • Shaikh, Faraz
  • Al Karim, Miftah

Abstract

This study presents a methodology for assessing the potential solar resource available at solar-powered parking machines installed in a typical city center. It is based on capturing fisheye images through an appropriate camera/lens. These images are then processed to classify the sky and non-sky pixels using a semi-automated sky detection method, also developed in this study. The processed images are then used to calculate the diffuse and beam solar potential of the site by estimating the visible fraction of the sky and the hours of the year during which the sun can be seen from the site, respectively. For the case study, 15 existing parking machines were analyzed in the central business district of Auckland (New Zealand). It was found that most of these machines were able to capture less than 50% of the available solar potential. Only two machines crossed the yearly irradiation threshold that represents the criterion for solar panel installation. If modern high-power solar-powered machines are used, selecting suitable locations for installation is crucial.

Suggested Citation

  • Rehman, Naveed ur & Katebi, Milad & Shaikh, Faraz & Al Karim, Miftah, 2020. "Solar resource assessment of modern parking machines in an urban environment," Renewable Energy, Elsevier, vol. 149(C), pages 1406-1413.
    • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:1406-1413
    DOI: 10.1016/j.renene.2019.10.131
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    References listed on IDEAS

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    1. Bosch, J.L. & Batlles, F.J. & Zarzalejo, L.F. & López, G., 2010. "Solar resources estimation combining digital terrain models and satellite images techniques," Renewable Energy, Elsevier, vol. 35(12), pages 2853-2861.
    2. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
    3. Suomalainen, Kiti & Wang, Vincent & Sharp, Basil, 2017. "Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level," Renewable Energy, Elsevier, vol. 111(C), pages 463-475.
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