Forecasting high-frequency spatio-temporal wind power with dimensionally reduced echo state networks

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.

Automated summary

Accurate hourly forecasts of wind speed and power are crucial for energy budget planning. This study proposes a machine learning approach to model the nonlinear wind dynamics, resulting in an improved 2-h-ahead forecasted power by 11% in Saudi Arabia.