Forecasting and Downscaling Solar Irradiance Using Transformers

Music City Center 

As solar energy continues to grow as a key component of the global energy mix, accurate forecasting of solar irradiance becomes more crucial for ensuring reliable electricity supply. However, existing forecasting methods often fail to capture the fine temporal variations in solar irradiance, particularly in regions where local weather conditions play a significant role. This research addresses the growing need for accurate solar irradiance forecasting to optimize the integration of solar energy into the grid. By using raw data, we aim to preserve important short-term fluctuations that are crucial for precise forecasts. The focus was on downscaling global solar irradiance data from a 15-min. resolution to a higher, 5-min. local resolution for Brookings, South Dakota. A transformer-based model was applied to forecast solar power output, utilizing different approaches to assess the effectiveness of various downscaling methods. The model was trained on historical data and used to generate short-term forecast for 24 hours, with performance evaluated based on standard error metrics. The findings highlight the potential of transformer models for improving solar irradiance forecast.

Solar Irradiance Forecast

Downscaling

Transformers

Time Series 

ENAR