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

Huang Huang, Stefano Castruccio, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Bibliographical note

KAUST Repository Item: Exported on 2022-01-25
Acknowledged KAUST grant number(s): OSR-2018-CRG7-3742.
Acknowledgements: King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR). Grant Number: OSR-2018-CRG7-3742.

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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