RenewBench: Real Energy Data You Can Actually Use

Transitioning to renewable energy is essential for mitigating climate change, but the variable and decentralised nature of such generation systems presents major challenges when maintaining grid stability for reliable operation. AI-driven solutions have the potential to address these challenges, particularly in the form of more powerful and robust forecasting. However, progress at scale is hampered by the lack of standardised, high-quality renewable energy datasets. Existing models are therefore often limited in geographic scope, restricted to a specific generation or data type, and evaluated on proprietary datasets that prevent broad comparison. Additionally, these models disregard the spatio-temporal couplings influencing long-term grid stability, as they consider only local weather inputs or ignore weather dependencies altogether.

RenewBench addresses these limitations by fusing renewable energy generation and weather data to create a global, open-source energy benchmark. We consolidate openly available generation datasets with high temporal and spatial resolution into a standardised Zarr-based structure. These are combined with meteorological reanalysis data and enriched with comprehensive SpatioTemporal Asset Catalog (STAC) metadata to create an AI-ready findable, accessible, interoperable, and reusable (FAIR) dataset. ... mehrBy leveraging a STAC FastAPI and PgSTAC backend, researchers can perform nearly instantaneous metadata queries and automated retrieval via a dedicated Python package to facilitate many benchmarking tasks.

In this poster we present the current status of RenewBench, including incorporated geographic regions, database setup, and initial benchmarking ideas. By building this open and unified global benchmark, we contribute to democratising data access for the next generation of data-driven energy-meteorology solutions.