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.

中文翻译：

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使用基于张量序列的高阶动态模式分解来预测风能

随着国际能源市场越来越重视清洁能源的发展，风电也逐渐受到各国的重视。风能是一种可持续且环保的能源。然而，它是不稳定的。因此，开发其预测算法非常重要。在本文中，我们应用了一种最近开发的算法，该算法有效地将张量序列分解与高阶动态模式分解（TT-HODMD）结合起来。动态模式分解（DMD）是一种数据驱动技术，不需要事先的数学模型。它基于测量数据或时隙。正如所证明的，对于预测来说，使用高阶 DMD (HODMD) 非常重要。反过来，HODMD 可能会导致非常大规模的稀疏阵列。张量序列分解提供了一种处理此类数组的高效方法。事实证明，组合的 TT-HODMD 算法能够对未来几个月的风电功率提供相当准确的预测。