AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas ...

• Main Authors: Ye, Feng, Brodie, Joseph, Miles, Travis, Ezzat, Ahmed Aziz
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Applications stat.AP; Systems and Control eess.SY; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; North Atlantic
• Online Access: https://dx.doi.org/10.48550/arxiv.2303.02246; https://arxiv.org/abs/2303.02246

The reliable integration of wind energy into modern-day electricity systems heavily relies on accurate short-term wind forecasts. We propose a spatio-temporal model called AIRU-WRF (short for the AI-powered Rutgers University Weather Research & Forecasting), which fuses numerical weather predictions (NWPs) with local observations in order to make wind speed forecasts that are short-term (minutes to hours ahead), and of high resolution, both spatially (site-specific) and temporally (minute-level). In contrast to purely data-driven methods, we undertake a "physics-guided" machine learning approach which captures salient physical features of the local wind field without the need to explicitly solve for those physics, including: (i) modeling wind field advection and diffusion via physically meaningful kernel functions, (ii) integrating exogenous predictors that are both meteorologically relevant and statistically significant; and (iii) linking the multi-type NWP biases to their driving meso-scale weather ...