  Fast and accurate hourly forecasts of wind speed and power are crucial in
quantifying and planning the energy budget in the electric grid. Modeling 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 modeling 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
the spatial dimensionality. Our results show that for locations highlighted as
wind abundant by a previous work, our approach results in a 11% improvement in
the two-hours-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.
