Environmental Science and Pollution Research

, Volume 25, Issue 16, pp 15484–15491 | Cite as

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error

  • Lisa B. BosmanEmail author
  • Seth B. Darling
Research Article


The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.


Solar Photovoltaic Debris Snow Loss Derate 



This work was performed, in part, at the Center for Nanoscale Materials, a U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences User Facility, under contract no. DE-AC02-06CH11357.

Funding information

This research has been supported, in part, by the National Aeronautics and Space Administration under Grant No. NNX14AG57A issued through the Education Opportunities in NASA STEM (EONS) program for NICE-T, the National Science Foundation under Grant No. HRD-1417582 issued through the NSF Catalyzing Opportunities for Research and Education TCUP program, and the Environmental Protection Agency under Grant No. 83696601.


  1. Andrews RW, Pearce JM (2012) Prediction of energy effects on photovoltaic systems due to snowfall events. In: Photovoltaic Specialists Conference (PVSC), 2012 38th IEEE. IEEE, pp 003386–003391Google Scholar
  2. Andrews RW, Pearce JM (2013) The effect of spectral albedo on amorphous silicon and crystalline silicon solar photovoltaic device performance. Sol Energy 91:233–241. CrossRefGoogle Scholar
  3. Andrews RW, Pollard A, Pearce JM (2013) The effects of snowfall on solar photovoltaic performance. Sol Energy 92:84–97. CrossRefGoogle Scholar
  4. Azimoh CL, Wallin F, Karlsson B (2014) An assessment of unforeseen losses resulting from inappropriate use of solar home systems in South Africa. Appl Energy 136:336–346CrossRefGoogle Scholar
  5. Bonkaney AL, Madougou S, Adamou R (2017) Impacts of cloud cover and dust on the performance of photovoltaic module in Niamey. J Renew Energy 2017:9107502, 8 pages. CrossRefGoogle Scholar
  6. Bosman L (2014) A decision support system to analyze, predict, and evaluate solar energy system performance: PVSysCO (photovoltaic System Comparison). Theses and Dissertations. 666.
  7. Brennan MP, Abramase AL, Andrews RW, Pearce JM (2014) Effects of spectral albedo on solar photovoltaic devices. Sol Energy Mater Sol Cells 124:111–116. CrossRefGoogle Scholar
  8. Clean Energy Decision Support Centre (2004) Clean energy project analysis: RETScreen engineering and cases textbook. Natural Resources CanadaGoogle Scholar
  9. Clean Energy Decision Support Centre (2005) RETScreen software online user manual photovoltaic project model. Natural Resources CanadaGoogle Scholar
  10. Darling S, You F (2013) The case for organic photovoltaics. RSC Adv 3:17633CrossRefGoogle Scholar
  11. Darling S, You F, Veselka T, Velosa A (2011) Assumptions and the levelized cost of energy for photovoltaics. Energy Environ Sci 4:3133–3139CrossRefGoogle Scholar
  12. Dobos AP (2014) PVWatts version 5 manual. Golden, National Renewable Energy Laboratory (NREL)CrossRefGoogle Scholar
  13. Energy Matters LLC (2009) Solar and wind estimator assumptions and system sizing result comparisons.
  14. Faiman (2008) Assessing the outdoor operating temperature of photovoltaic modules. Prog Photovolt 16:307–315CrossRefGoogle Scholar
  15. Garcia MCA, Balenzategui JL (2004) Estimation of photovoltaic module yearly temperature and performance based on nominal operation cell temperature calculations. Renew Energy 29(12)Google Scholar
  16. Hall, Prairie, Anderson, Boes (1978) Generation of typical meterological years for 26 SOLMET stations. SAND78–1601. Sandia National Laboratories, AlbuquerqueGoogle Scholar
  17. Heidari N, Gwamuri J, Townsend T, Pearce JM (2015) Impact of snow and ground interference on photovoltaic electric system performance. IEEE J Photovoltaics 5:1680–1685. CrossRefGoogle Scholar
  18. Hong T, Change W-K, Lin H-W (2013) A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data. Appl Energy 111:333–350CrossRefGoogle Scholar
  19. King, Boyson, Kratochvil (2004) Photovoltaic array performance model. Albuquerque, Sandia National LaboratoriesGoogle Scholar
  20. Klise G, Stein J (2009) Models used to assess the performance of photovoltaic systems. SANDIAGoogle Scholar
  21. Long H, Zhang Z, Su Y (2014) Analysis of daily solar power prediction with data-driven approaches. Appl Energy 126:29–37CrossRefGoogle Scholar
  22. Loutzenhiser, Manz, Felsmann, Strachan, Frank, Maxwell (2007) Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation. Sol Energy 81:254–267CrossRefGoogle Scholar
  23. Marion B (2008) Comparison of predictive models for photovoltaic module performance. In: Photovoltaic Specialists Conference, 2008. PVSC ‘08. 33rd IEEE, 11–16 May 2008. pp 1–6. doi:
  24. Marion, Urban (1995) Users manual for TMY2s typical meteorological years. National Renewable Energy LaboratoryGoogle Scholar
  25. Marion B, Schaefer R, Caine H, Sanchez G (2013) Measured and modeled photovoltaic system energy losses from snow for Colorado and Wisconsin locations. Sol Energy 97:112–121CrossRefGoogle Scholar
  26. National Renewable Energy Laboratory (2013) PV watts—derate factors. Obtained 07/27/2013
  27. National Renewable Energy Laboratory (2014) System Advisor Model (SAM) help system, Version 2014.1.14Google Scholar
  28. Perez, Seals, Ineichen, Stewart, Menicucci (1987) A new simplified version of the Perez diffuse irradiance model for tilted surfaces. Sol Energy 39:221–232CrossRefGoogle Scholar
  29. Perez, Ineichen, Seals, Michalsky, Stewart (1990) Modeling daylight availability and irradiance components from direct and global irradiance. Sol Energy 44:271–289CrossRefGoogle Scholar
  30. Powers L, Newmiller J, Townsend T (2010) Measuring and modeling the effect of snow on photovoltaic system performance. In: Photovoltaic Specialists Conference (PVSC), 2010 35th IEEE, 20–25 June 2010. pp 000973–000978. doi:
  31. Raugei M, Fullana-i-Palmer P, Fthenakis V (2012) The energy return on energy investment (EROI) of photovoltaics: methodology and comparisons with fossil fuel lifecycles. Energ Policy 45:576–582CrossRefGoogle Scholar
  32. Rizzo SA, Scelba G (2015) ANN based MPPT method for rapidly variable shading conditions. Appl Energy 145:124–132CrossRefGoogle Scholar
  33. Ryberg D, Freeman J (2015) Integration, validation, and application of a PV snow coverage model in SAM. National Renewable Energy Lab.(NREL), GoldenCrossRefGoogle Scholar
  34. Su Y, Chan L-C, Shu L, Tsui K-L (2012) Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems. Appl Energy 93:319–326CrossRefGoogle Scholar
  35. TamizhMani G, Ji L, Tang Y, Petacci L, Osterwald C (2003) Photovoltaic module thermal/wind performance: long-term monitoring and model development for energy rating. Paper presented at the NCPV and Solar Program Review Meeting 2003Google Scholar
  36. Thevenard D, Pelland S (2013) Estimating the uncertainty in long-term photovoltaic yield predictions. Sol Energy 91:432–445. CrossRefGoogle Scholar
  37. U.S. Energy Information Administration (2015) Annual energy outlook 2015 with projections to 2040 vol DOE/EIA-0383(2015)Google Scholar
  38. Wilcox S, Marion W (2008) Users manual for TMY3 data sets, Technical Report NREL/TP-581-43,156. National Renewable Energy LaboratoryGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.Argonne National LaboratoryLemontUSA

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