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Prediction of wind energy with the use of tensor‐train based higher order dynamic mode decomposition

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  • Keren Li
  • Sergey Utyuzhnikov

Abstract

As the international energy market pays more and more attention to the development of clean energy, wind power is gradually attracting the attention of various countries. Wind power is a sustainable and environmentally friendly resource of energy. However, it is unstable. Therefore, it is important to develop algorithms for its prediction. In this paper, we apply a recently developed algorithm that effectively combines the tensor train decomposition with the higher order dynamic mode decomposition (TT‐HODMD). The dynamic mode decomposition (DMD) is a data‐driven technique that does not need a prior mathematical model. It is based on the measurement data or time slots. As demonstrated, for prediction it is important to use the higher order DMD (HODMD). In turn, HODMD might lead to very large scale arrays that are sparse. The tensor train decomposition provides a highly efficient way to work with such arrays. It is demonstrated that the combined TT‐HODMD algorithm is capable of providing quite accurate prediction of wind power for months ahead.

Suggested Citation

  • Keren Li & Sergey Utyuzhnikov, 2024. "Prediction of wind energy with the use of tensor‐train based higher order dynamic mode decomposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2434-2447, November.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:7:p:2434-2447
    DOI: 10.1002/for.3126
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    1. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    2. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    3. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    4. Keren Li & Sergey Utyuzhnikov, 2023. "Tensor Train-Based Higher-Order Dynamic Mode Decomposition for Dynamical Systems," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    5. Sovacool, Benjamin K., 2009. "The intermittency of wind, solar, and renewable electricity generators: Technical barrier or rhetorical excuse?," Utilities Policy, Elsevier, vol. 17(3-4), pages 288-296, September.
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