See also: Snow effects on PV Lit review

E. Andenæs, B. P. Jelle, K. Ramlo, T. Kolås, J. Selj, and S. E. Foss, “The influence of snow and ice coverage on the energy generation from photovoltaic solar cells,” Solar Energy, vol. 159, pp. 318–328, Jan. 2018.

  • Examines properties of snow with respect to reflectance (albedo) and spectral transmittance
  • Examines common transmittance profile
  • Snow loads and snowmelt risk assessment carried out
  • Common challenges relating to snow and effects on material as well as architecture checked

D. Ryberg and J. Freeman, “Integration Validation and Application of a PV Snow Coverage Model in SAM,” . 2015.

  • builds on and integrates Marion's model into NREL's System Advisor Model (SAM)
  • model is effective for reducing estimation errors for PV arrays
  • estimates average snow loss in United States using the new functionality in SAM together with historical data set

B. Marion, R. Schaefer, H. Caine, and G. Sanchez, “Measured and modeled photovoltaic system energy losses from snow for Colorado and Wisconsin locations,” Solar Energy, vol. 97, pp. 112–121, Nov. 2013.

  • Measures energy losses due to snow for these areas for winter periods in two year
  • Result shows 90% monthly energy loss due to snow, representing up to 12% annual energy loss
  • Introduces new model and method for energy losses using variable factors such snow depth, irradiance and air temperature relationship, PV tilt angle and extent of snow coverage that affect PV power production
  • Compares result of measured with modelled energy losses

R. W. Andrews, A. Pollard, and J. M. Pearce, “The effects of snowfall on solar photovoltaic performance,” Solar Energy, vol. 92, pp. 84–97, Jun. 2013.

  • Discusses effect of snow on PV performance using multi-angle and multi-technological approach
  • Introduces a novel methodology for measuring snow losses on 5 minutes time series resolution
  • develops new method for probability distribution of snow precipitation
  • Results show snow loss dependencies on albedo, tilt angle and technology system typology deployed
  • Improvement of system performance is contingent on proper snow loss assessment

R. W. Andrews and J. M. Pearce, “Prediction of energy effects on photovoltaic systems due to snowfall events,” in 2012 38th IEEE Photovoltaic Specialists Conference, 2012, pp. 003386–003391.

  • Provides snow fall effects identification method
  • Recommends a model for prediction of snow effects using meteorological time series
  • Model validated with data from two existing large PV plants greater than 8 MW
  • Daily and average snow effect was predicted for plants with low tilt angle

E. Lorenz, D. Heinemann, and C. Kurz, “Local and regional photovoltaic power prediction for large scale grid integration: Assessment of a new algorithm for snow detection,” Progress in Photovoltaics: Research and Applications, vol. 20, no. 6, pp. 760–769, 2012.

  • Resolution of hourly weather forecast for a 2-day period deployed as a basic approach to predicting regional PV power
  • Presents new and enhanced features of the regional power forecasting system of the Oldenburg University and the Meteocontrol GmBH
  • New PV power prediction approach improves on existing overestimation of power production during snow cover
  • Forecast for 1-year period was carried out
  • Results show reduced root mean square error (rmse), from 4.9% to 3.9% and 5.7% to 4.6% for intra-day and day-ahead forecasts respectively
  • Using proposed algorithm for snow prediction provides greatest reduced rmse in January

L. Powers, J. Newmiller, and T. Townsend, “Measuring and modeling the effect of snow on photovoltaic system performance,” in 2010 35th IEEE Photovoltaic Specialists Conference, 2010, pp. 000973–000978.

  • Provided a side by side PV test bed installed in California
  • Gauges energy loss for different and most common tilt angles
  • test result presented for a particular winter period

Discussion[View | Edit]

You may want to merge with Snow effects on PV Lit review --Joshua Pearce (talk) 03:22, 23 January 2019 (PST)

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