AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. North Atlantic Offshore Wind Energy Areas ...
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 predict...
Main Authors: | , , , |
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Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
arXiv
2023
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Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.2303.02246 https://arxiv.org/abs/2303.02246 |
Summary: | 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 ... |
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