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Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data

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  • Lukač, Niko
  • Žlaus, Danijel
  • Seme, Sebastijan
  • Žalik, Borut
  • Štumberger, Gorazd

Abstract

The roof surfaces within urban areas are constantly attracting interest regarding the installation of photovoltaic systems. These systems can improve self-sufficiency of electricity supply, and can help to decrease the emissions of greenhouse gases throughout urban areas. Unfortunately, some roof surfaces are unsuitable for installing photovoltaic systems. This presented work deals with the rating of roof surfaces within urban areas regarding their solar potential and suitability for the installation of photovoltaic systems. The solar potential of a roof’s surface is determined by a new method that combines extracted urban topography from LiDAR data with the pyranometer measurements of global and diffuse solar irradiances. Heuristic annual vegetation shadowing and a multi-resolution shadowing model, complete the proposed method. The significance of different influential factors (e.g. shadowing) was analysed extensively. A comparison between the results obtained by the proposed method and measurements performed on an actual PV power plant showed a correlation agreement of 97.4%.

Suggested Citation

  • Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.
    • Handle: RePEc:eee:appene:v:102:y:2013:i:c:p:803-812
    DOI: 10.1016/j.apenergy.2012.08.042
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    References listed on IDEAS

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    1. Kabalci, Ersan, 2011. "Development of a feasibility prediction tool for solar power plant installation analyses," Applied Energy, Elsevier, vol. 88(11), pages 4078-4086.
    2. Zejli, Driss & Ouammi, Ahmed & Sacile, Roberto & Dagdougui, Hanane & Elmidaoui, Azzeddine, 2011. "An optimization model for a mechanical vapor compression desalination plant driven by a wind/PV hybrid system," Applied Energy, Elsevier, vol. 88(11), pages 4042-4054.
    3. Bekele, Getachew & Tadesse, Getnet, 2012. "Feasibility study of small Hydro/PV/Wind hybrid system for off-grid rural electrification in Ethiopia," Applied Energy, Elsevier, vol. 97(C), pages 5-15.
    4. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    5. Zogou, Olympia & Stapountzis, Herricos, 2011. "Energy analysis of an improved concept of integrated PV panels in an office building in central Greece," Applied Energy, Elsevier, vol. 88(3), pages 853-866, March.
    6. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    7. Agrawal, Basant & Tiwari, G.N., 2010. "Optimizing the energy and exergy of building integrated photovoltaic thermal (BIPVT) systems under cold climatic conditions," Applied Energy, Elsevier, vol. 87(2), pages 417-426, February.
    8. 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.
    9. Vats, Kanchan & Tiwari, G.N., 2012. "Energy and exergy analysis of a building integrated semitransparent photovoltaic thermal (BISPVT) system," Applied Energy, Elsevier, vol. 96(C), pages 409-416.
    10. Tiwari, G.N. & Mishra, R.K. & Solanki, S.C., 2011. "Photovoltaic modules and their applications: A review on thermal modelling," Applied Energy, Elsevier, vol. 88(7), pages 2287-2304, July.
    11. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
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