AIRU-WRF: A physics-guided spatio-temporal wind forecasting model and its application to the U.S. Mid Atlantic offshore wind energy areas

Feng Ye, Joseph Brodie, Travis Miles and Ahmed Aziz Ezzat

Renewable Energy, 2024, vol. 223, issue C

Abstract: 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 (ML) 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. Mid 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.

Keywords: Offshore wind energy; Physics-informed learning; Probabilistic forecasting; Spatio-temporal modeling (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:223:y:2024:i:c:s0960148123018499

DOI: 10.1016/j.renene.2023.119934

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