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,” https://www.sciencedirect.com/science/article/pii/S0038092X17309581 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
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,” https://www.sciencedirect.com/science/article/pii/S0038092X13003034 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
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,” https://onlinelibrary.wiley.com/doi/full/10.1002/pip.1224 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