EUPVSec 2026: 3-Month Nordic Benchmark of ASI-Based Solar Nowcasting: Commercial Solutions and DL Literature Models

Kurzfassung

Short-term irradiance forecasting using all-sky imagers (ASI) is useful for efficient PV plant control, grid balancing, and high-frequency energy trading. Yet knowing which solution is optimal based on the application and location is challenging due to a scarcity of solid comparisons between forecasting models. Intercomparison of models in the literature based on standard metrics is difficult, as the location and atmospheric conditions of the site play a major role in the scale of the errors of a forecast. Even metrics using normalization (nRMSE or nMAE) and metrics using a reference model (Skill Score) still suffer from this sensitivity. The only way to truly compare different forecasting solutions is through common datasets or benchmarking campaigns, ensuring that the solutions are evaluated on the same task in the same time period. To this end, we present a 3-month benchmark (21 May–21 August) at 60◦N in Kjeller, Norway, designed to assess the state-of-the-art (SOTA) scientific and commercial forecasting models. Three generations of scientific forecasting models are included in this benchmark: (i) SUNSET (CNN-architecture), (ii) a Transformer end-to-end model, and (iii) at least one SOTA generative model, to be evaluated for the final contribution. A defining aspect of this study is the inclusion of two commercial ASI forecasting products in addition to the literature models, evaluating the range of available nowcasting solutions. The evaluation covers RMSE, MAE, Skill Score (SS), and a ramp-detection metric. Preliminary results show that the Transformer model consistently outperforms smart persistence and the commercial solutions and other research models on all metrics for all horizons, except for the ramp prediction metric, where it underperforms all other models. Performance analysis across clear-sky index (k) and variability index (V ) indicates that the atmospheric conditions strongly influences performance.