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Workshop: Tackling Climate Change with Machine Learning

EnhancedSD: Predicting Solar Power Reanalysis from Climate Projections via Image Super-Resolution

Nidhin Harilal · Bri-Mathias Hodge · Claire Monteleoni · Aneesh Subramanian


Renewable energy-based electricity systems are seen as a keystone of future decarbonization efforts. However, power system planning does not currently consider the impacts of climate change on renewable energy resources such as solar energy, chiefly due to a paucity of climate-impacted high-resolution solar power data. Existing statistical downscaling (SD) methods that learn to map coarse-resolution versions of historical reanalysis to generate finer resolution outputs are of limited use when applied to future climate model projections due to the domain gap between climate models and reanalysis. In contrast, we present EnhancedSD, a deep learning-based framework for downscaling coarse-scale climate model outputs to high-resolution observational (reanalysis) data. Our proposed approach toward SD allows for future reanalysis projections, which can be pivotal for mitigating climate change’s impacts on power systems planning.

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