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

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  • Huang Huang
  • Stefano Castruccio
  • Marc G. Genton

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.

Suggested Citation

  • Huang Huang & Stefano Castruccio & Marc G. Genton, 2022. "Forecasting high‐frequency spatio‐temporal wind power with dimensionally reduced echo state networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 449-466, March.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:2:p:449-466
    DOI: 10.1111/rssc.12540
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    References listed on IDEAS

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    1. Richard Kinley, 2017. "Climate change after Paris: from turning point to transformation," Climate Policy, Taylor & Francis Journals, vol. 17(1), pages 9-15, January.
    2. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
    3. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    4. Patrick L. McDermott & Christopher K. Wikle, 2019. "Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting," Environmetrics, John Wiley & Sons, Ltd., vol. 30(3), May.
    5. Crippa, Paola & Alifa, Mariana & Bolster, Diogo & Genton, Marc G. & Castruccio, Stefano, 2021. "A temporal model for vertical extrapolation of wind speed and wind energy assessment," Applied Energy, Elsevier, vol. 301(C).
    6. Christopher Wikle & Mevin Hooten, 2010. "Rejoinder on: A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 466-468, November.
    7. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M., 2007. "Wind power resource assessment for Rafha, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 937-950, June.
    8. Xinxin Zhu & Marc G. Genton, 2012. "Short‐Term Wind Speed Forecasting for Power System Operations," International Statistical Review, International Statistical Institute, vol. 80(1), pages 2-23, April.
    9. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    10. Giani, Paolo & Tagle, Felipe & Genton, Marc G. & Castruccio, Stefano & Crippa, Paola, 2020. "Closing the gap between wind energy targets and implementation for emerging countries," Applied Energy, Elsevier, vol. 269(C).
    11. Petra Kuhnert & D.W. Gladish & C.K. Wikle, 2014. "Physically motivated scale interaction parameterization in reduced rank quadratic nonlinear dynamic spatio‐temporal models," Environmetrics, John Wiley & Sons, Ltd., vol. 25(4), pages 230-244, June.
    12. Felipe Tagle & Stefano Castruccio & Paola Crippa & Marc G. Genton, 2019. "A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(3), pages 312-326, May.
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