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• W4207045811 abstract "Abstract Fast 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." @default.
• W4207045811 created "2022-01-26" @default.
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• W4207045811 date "2022-03-01" @default.
• W4207045811 modified "2023-10-11" @default.
• W4207045811 title "Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks" @default.
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• W4207045811 doi "https://doi.org/10.1111/rssc.12540" @default.
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