1. Prediction of Energy Effects on Photovoltaic Systems due to Snowfall Events Andrews, Rob W., and Joshua M. Pearce. “Prediction of Energy Effects on Photovoltaic Systems Due to Snowfall Events.” 2012 38th IEEE Photovoltaic Specialists Conference, 2012. doi:10.1109/PVSC.2012.6318297. 
- Modules of C-Si installed at different angles in an Open Source Outdoors Test Field (OSOTF) and snowfall data is collected from Kingston climate weather station for the winters of 2010/2011 and 2011/2012.
- Data from two other solar firms SF1 and SF2 collected hourly: DC power input to each inverter, Solar irradiation and module temperature measurements.
- Assumption: Upon performing a sensitivity analysis using the RMSE of the model, relative humidity and wind speed were considered not signiﬁcant and the magnitude of energy gain or loss from snowfall is proportional to the mean solar irradiation in a given day.
- A comparison of the derived model coefﬁcients from the three sources used. In order to test the applicability of this approach, the snow losses for SF2 were determined using the coefﬁcients derived from SF1 and the OSOTF.
- Predictions can be made by integrating data from a geographically dissimilar system of a similar conﬁguration by using this proposed method.
2. The Effects of Snowfall on Solar Photovoltaic Performance
Andrews, Rob W., Andrew Pollard, and Joshua M. Pearce. “The Effects of Snowfall on Solar Photovoltaic Performance.” Solar Energy 92 (2013): 84–97. doi:10.1016/J.SOLENER.2013.02.014. 
- Snowfall accumulation is affected by ambient temperature (above and below -3◦C), wind speeds, inclination from the horizontal, and surface properties.
- Giddings and LaChappelle and Bouger-Lambert law infers that approximately 20% of incident radiation will be available at 2cm snow depth, and 3-4% is available at 10cm depth.
- Transmitted light from snow layer is short-wave radiation causes shedding phenomenon of snow due to its insulation properties same as a fiber glass, thus retain heat and form water later leading to snow slide.
- Energy inﬂux to a snow-covered module can occur in three ways:
1. Diﬀusion of short wave radiation through the snow pack, 2. Albedo reﬂection to the exposed rear of the module, 3. Conduction from other parts of the PV array that are not covered with snow.
- 70 modules of amorphous silicon and crystalline silicon at arranged at 5◦, 10◦, 15◦, 20◦, 40◦, and 60◦ are monitored for short-circuit current and back temperature.
- change in the short circuit current will have a proportional change on the power output of a module, as it represents the level of light reaching the modules, making it an appropriate performance metric, while eﬀectively isolating against the eﬀects of temperature on the results.
- Yearly snow eﬀect is deﬁned as the summation of the diﬀerence between the actual and synthetic output.
- Albedo eﬀect increases with module inclination angle,which is due to the increased view factor from the module to the snow surface
- Lower temperature and higher relative humidity will tend to increase the time to shed.
3. Photovoltaics and snow: An update from two winters of measurements in the SIERRA
Tim Townsend, BEW Engineering, San Ramon, CA, U.S.A. and Loren Powers, BEW Engineering, San Ramon, CA, U.S.A. 19 April 2012 
- Three pairs of photovoltaic (PV) modules at fixed south-facing tilt angles of 0°, 24° and 39° were installed in Truckee, CA (near Lake Tahoe) at the beginning of the 2009–10 winter. And it receives 200 inches per year (5 m) of snow. Three are manually cleaned and heated thermostatically while other three are bordered and allowed to shed naturally.
- Snow losses are gauged as the difference in monthly amp-hours between the clean and uncleaned modules
- In 2009-10, wintertime energy losses of 40–60% and annual energy losses from 12–18% were noted at normal snow fall.
- Model Development equation accounts for ground interference, air temperature, plane of array insolation and relative humidity.
- In addition to the BEW coefficients and site latitude, the only data needed to run the model are: Monthly snowfall, Number of snow events per month, Average air temperature, Plane of array insolation, Average relative humidity. The monthly loss estimates in the table given can be used directly as inputs to popular PV simulation programs such as PVSyst.
4. Energy efficiency and renewable energy under extreme conditions: Case studies from Antarctica
TinaTin Antarctic and Southern Ocean Coalition, BP 80358, 45163 Olivet, CEDEX 3, France, Benjamin K.Sovacool, National University of Singapore, Singapore, David Blake British Antarctic Survey, United Kingdom, Peter Magill, Australian Antarctic Division, Australia, Saad, Alfred Wegener Institute, Germany NaggareSvenLidstrom, Swedish Polar Research Secretariat, Sweden, Kenji Ishizawag National Institute of Polar Research, Japan. Johan Berte, International Polar Foundation, Belgium. Received 20 July 2009, Accepted 14 October 2009, Available online 3 November 2009. 
- Solar energy and combined systems : In most cases, solar power is combined with wind turbines and diesel generators to meet energy needs in Antarctica.
- Field camps and instrumentation: Power systems based upon solar panels and sometimes small wind turbines allow instruments to collect data continuously and to connect to satellites for remote access and data transfer
- Applications: Four 35 W solar panels and a 12 V battery provide the power for a weighbridge that weighs each penguin as it leaves its colony.
- Costs and benefits of analysis for setting up renewable energy sources in Antarctica.
5. A Low Cost Method of Snow Detection on Solar Panels and Sending Alerts
Seyedali Meghdadi, Electrical Engineering Faculty, Memorial University of Newfoundland, NL,and Tariq Iqbal, Faculty of Engineering and Applied Science, Memorial University of Newfoundland Canada, Journal of Clean Energy Technologies, Vol. 3, No. 5, September 2015. 
- Arduino Uno software for design and modelling the circuit.
- Algorithm and system overview