SolarBench is an initiative focused on reducing barriers for researchers and practitioners to access large environmental datasets for advancing solar energy forecasting and related fields.

This repo contains multiple data modalities and is structured as follow:

• ground_full: Ground-based sensor data including sky images, a variety of meteorological measurements, and labels for solar forecasting tasks, namely different solar irradiance components (i.e., GHI, DHI, and DNI) or power generation from solar panels.
• ground_no_skycam: A lite version of ground_full by excluding sky images. Useful for fast analysis and training non-vision models or performing ablation studies.
• satellite: Geostationary satellite imagery including GOES, Himawari, MSG.
• weather_forecasts: Numerical weather predictions (e.g., GFS).
• climate_reanalysis: Retrospective climate reanalysis datasets (e.g., ERA5).

These data can be easily accessed and made into use through the machine learning toolbox we developed. For more details, check it out here: