Speaker
Description
AI-driven Weather Prediction Models (AIWPMs) are revolutionizing weather forecasting and surpassing traditional numerical weather models in both accuracy and computational efficiency. With weather conditions impacting numerous sectors, such as renewable energy generation, this has far-reaching implications. AIWPMs could enable improved forecasts for renewable energy generation, such as wind power, ensuring that appropriate actions are taken to maintain grid stability and cope with volatile generation. Traditionally, such renewable energy forecasts have been generated with separate models in a model-chain approach, considering weather predictions as important inputs. We explore novel strategies for utilizing AIWPMs for wind energy prediction, including parameter-efficient fine-tuning methods, which have been successfully employed to adapt large models to new tasks without excessive computational cost in the machine learning community.
