  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 combines 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 mesoscale weather conditions. Tested on real-world data from the
U.S. North Atlantic where several offshore wind projects are in-development,
AIRU-WRF achieves notable improvements, in terms of both wind speed and power,
relative to various forecasting benchmarks including physics-based, hybrid,
statistical, and deep learning methods.
