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Correlation of actual efficiency of photovoltaic panels with air mass

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  • Roumpakias, Elias
  • Zogou, Olympia
  • Stamatelos, Anastassios

Abstract

Photovoltaic installations are already producing more than 5% of electricity in several European countries. Monitoring of photovoltaic parks produces a huge amount of data that are employed in checking the real-world efficiency of photovoltaic panels in a variety of locations, for different atmospheric and environmental conditions. The aim of this paper is to present real-world efficiency data for photovoltaic panels and photovoltaic parks, as function of air mass and environmental conditions. The PV efficiency deteriorates quickly when the solar altitude is less than 45° and the solar insolation drops below 200 W/m2. This affects the accuracy of prediction of overall efficiency of PV parks and deserves more attention and study.

Suggested Citation

  • Roumpakias, Elias & Zogou, Olympia & Stamatelos, Anastassios, 2015. "Correlation of actual efficiency of photovoltaic panels with air mass," Renewable Energy, Elsevier, vol. 74(C), pages 70-77.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:70-77
    DOI: 10.1016/j.renene.2014.07.051
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    References listed on IDEAS

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    1. Alonso García, M.C. & Balenzategui, J.L., 2004. "Estimation of photovoltaic module yearly temperature and performance based on Nominal Operation Cell Temperature calculations," Renewable Energy, Elsevier, vol. 29(12), pages 1997-2010.
    2. Santos, J.M. & Pinazo, J.M. & Cañada, J., 2003. "Methodology for generating daily clearness index index values Kt starting from the monthly average daily value K̄t. Determining the daily sequence using stochastic models," Renewable Energy, Elsevier, vol. 28(10), pages 1523-1544.
    3. Mellit, A. & Kalogirou, S.A. & Shaari, S. & Salhi, H. & Hadj Arab, A., 2008. "Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system," Renewable Energy, Elsevier, vol. 33(7), pages 1570-1590.
    4. Skoplaki, E. & Palyvos, J.A., 2009. "Operating temperature of photovoltaic modules: A survey of pertinent correlations," Renewable Energy, Elsevier, vol. 34(1), pages 23-29.
    5. Kumar, Ravinder & Umanand, L., 2005. "Estimation of global radiation using clearness index model for sizing photovoltaic system," Renewable Energy, Elsevier, vol. 30(15), pages 2221-2233.
    6. Zogou, Olympia & Stapountzis, Herricos, 2011. "Experimental validation of an improved concept of building integrated photovoltaic panels," Renewable Energy, Elsevier, vol. 36(12), pages 3488-3498.
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    1. Roumpakias, Elias & Stamatelos, Anastassios, 2019. "Performance analysis of a grid-connected photovoltaic park after 6 years of operation," Renewable Energy, Elsevier, vol. 141(C), pages 368-378.

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