Severe Wind Event Prediction with Multivariate Physics-Informed Deep Learning (Proposals Track)

Willa Potosnak (Carnegie Mellon University); Cristian I Challu (Carnegie Mellon University); Kin G. Olivares (Carnegie Mellon University); James K Miller (Carnegie Mellon University); Artur Dubrawski (Carnegie Mellon University)

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Hybrid Physical Models Extreme Weather Power & Energy Time-series Analysis


Wind turbines play a crucial role in combating climate change by harnessing the force of the wind to generate clean and renewable energy. One key factor in ensuring the long-term effectiveness of wind turbines is the reduction of operating costs due to maintenance. Severe weather events, such as extreme changes in wind, can damage turbines, resulting in costly maintenance and economic losses in power production. We propose a preliminary physics-informed deep learning model to improve predictions of severe wind events and a multivariate time series extension for this work.

Recorded Talk (direct link)