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Fast and accurate hourly forecasts of wind speed and power are crucial inquantifying and planning the energy budget in the electric grid. Modeling windat a high resolution brings forth considerable challenges given its turbulentand highly nonlinear dynamics. In developing countries, where wind farms over alarge domain are currently under construction or consideration, this is evenmore challenging given the necessity of modeling wind over space as well. Inthis work, we propose a machine learning approach to model the nonlinear hourlywind dynamics in Saudi Arabia with a domain-specific choice of knots to reducethe spatial dimensionality. Our results show that for locations highlighted aswind abundant by a previous work, our approach results in an 11% improvement inthe two-hour-ahead forecasted power against operational standards in the windenergy sector, yielding a saving of nearly one million US dollars over a yearunder current market prices in Saudi Arabia.

Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks