Abstract
Renewable energy sources are emerging as a crucial alternative to traditional energy sources, driven by the pressing need to reduce greenhouse gas emissions and mitigate the effects of climate change. Accurate forecasting of renewable energy resources is essential for effective decision-making in the energy sector, particularly in deeply decarbonized energy systems. Machine learning (ML) can play a significant role in improving the accuracy of renewable energy forecasting by integrating it with numerical weather prediction (NWP) models, known as physics-informed ML. This approach can address the challenge of the poor extrapolation/generalization capability of ML models by leveraging the foundation of physics-based models to generalize better to new situations. This project aims to develop a novel physics-informed ML model by integrating physical equations from NWP models with ML models to enhance the accuracy and reliability of renewable energy forecasting, focusing on wind and solar energy production forecasting. The successful implementation of this model has the potential to promote the sustainability of the energy system, lower balancing costs, and combat climate change.
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