Solar panel installation is becoming increasingly common in areas with significant snowfall.
While PV systems produce energy year-round, snow accumulation on solar panels can limit production. Although there are models available to study snow on PV modules and several methods to calculate the energy loss due to snow, Clir leverages our data model to identify and categorize partial performance due to snowfall.
Clir's snow detector
Clir’s snow detector highlights any energy losses associated with snowfall on the panels. PV farms in climates that experience snowfall can potentially experience large amounts of underperformance due to snow cover on the panels.
The detector leverages data from multiple sources to detect if snow is present at the site and to categorize snow losses:
- Partial capability events of all inverters at the site.
- Ambient Temperature
- Daily performance ratio per inverter
- Irradiance threshold
When Clir’s solar platform identifies periods of underperformance at the farm, the events are automatically checked against snow conditions at the site. The events are also correlated by snow depth data either available from SCADA data or third-party weather services.
Benchmarking these losses against farms in similar regions can help owners decide how they should manage snowfall on the panels.
Snow detector use case: Clean scheduling
PV farms in climates that experience snow fall in the winter experience potentially large amounts of underperformance due to snow cover on solar panel. Asset managers in these situations are interested in understanding the magnitude of snow loss in winter months.
Leveraging the snow loss detectors, owners can understand and quantify losses associated with covered panels. By comparing the cost of these losses with the cost to remove snow or clear panels, owners can develop a cleaning schedule that minimizes operational costs and maximizes performance.
Clir evaluates site conditions during snow to identify potential performance, health issues or production loss. This help owners quantify the impact of snowfall at the farm to develop optimized and data-driven clean scheduling.
