Why forecast cloud shadows?

Solar forecasting can help solve a number of problems associated with solar energy harvesting:
  • High variability of the solar resource, due to movement of
    • the sun through the sky
    • clouds over the sun
  • Efficient and cost effective operation requires reliability and predictability.
  • Sharp changes can cause local voltage flicker.
  • Underestimating output can lead to balancing and frequency issues.

A number of solar forecasting methods currently exist. Their relevance varies with the size of the photovoltaic application and the desired forecast lead time.

Sky-imager forecasting, or cloud forecasting, is one of these methods. Images of the sky are captured from the ground at regular intervals and forecasts are made in real-time from the series of images.

Forecasting method Pros Cons
Physically-based - User-defined inputs and components
- Sharing made easy (invariant to geolocation and training)
- Suffers from insufficient data (inputs)
- Coarse resolution O(10km)
Satellite imaging - More direct measure of irradiance vs NWP
- Assimilates NWP
- Coarse resolution O(km)
- Requires visible light (IR would help with morning forecast accuracy)
Sky imaging - High spatial and temporal resolution O(min) - Does not account for cloud development and dissipation
- Limited to the SI FOV
Stochastic learning - Can assimilate all discussed methods - Extraneous or redundant inputs will give inaccurate results (ANNs and GAs can select inputs).