Journal
SOLAR ENERGY
Volume 228, Issue -, Pages 659-669Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2021.09.073
Keywords
Satellite-derived Solar Irradiance; Forecasting; Data-driven; Spatiotemporal Deep Learning
Categories
Funding
- Danske Commodities A/S
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The paper introduces a novel satellite-based neural network, IrradianceNet, for forecasting surface solar irradiance in Europe. The results demonstrate superior performance compared to several models, highlighting the feasibility of purely data-driven methods.
The presence of clouds is widely identified as the primary uncertainty in current surface solar global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived irradiance observations, only limited research has investigated this problem from a purely data-driven perspective, something that has seen tremendous success in related domains such as radar- and satellite-based precipitation short-term forecasting. This paper presents IrradianceNet, a novel satellite-based neural network for spatiotemporal forecasting of surface solar irradiance up to 4 h in the future over Europe. Our method is fully data-driven and needs no post-processing or calibration based on sparse ground-based measurements of irradiance. We demonstrate superior forecasting performance compared to several persistence models, the TV-L-1 algorithm, and ERAS reanalysis data for satellite-derived solar irradiance using the European SARAH-2.1 dataset. We also validate these results using ground-based pyranometer observations from the Baseline Surface Radiation Network. Our conclusions remain unchanged when we account for hourly and monthly seasonality. Finally, applying a simple cloud mask scheme, we demonstrate that our performance improvement arises due to a considerable reduction in cloudy pixel errors. This is initial evidence that purely data-driven methods might better approximate and infer future cloud dynamics and their impact on surface solar irradiance.
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