Forecasting high-frequency spatio-temporal wind power with dimensionally reduced echo state networks
KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2018-CRG7-3742.
Embargo End Date2023-01-23
Permanent link to this recordhttp://hdl.handle.net/10754/667220
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AbstractFast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid. Modelling wind at a high resolution brings forth considerable challenges given its turbulent and highly nonlinear dynamics. In developing countries, where wind farms over a large domain are currently under construction or consideration, this is even more challenging given the necessity of modelling wind over space as well. In this work, we propose a machine learning approach to model the nonlinear hourly wind dynamics in Saudi Arabia with a domain-specific choice of knots to reduce spatial dimensionality. Our results show that for locations highlighted as wind abundant by a previous work, our approach results in an 11% improvement in the 2-h-ahead forecasted power against operational standards in the wind energy sector, yielding a saving of nearly one million US dollars over a year under current market prices in Saudi Arabia.
CitationHuang, H., Castruccio, S., & Genton, M. G. (2022). Forecasting high-frequency spatio-temporal wind power with dimensionally reduced echo state networks. Journal of the Royal Statistical Society: Series C (Applied Statistics). doi:10.1111/rssc.12540
SponsorsKing Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR). Grant Number: OSR-2018-CRG7-3742.
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