Very short-term wind power forecasting considering static data: An improved transformer model

Sen Wang, Yonghui Sun, Wenjie Zhang, C.Y. Chung and Dipti Srinivasan

Energy, 2024, vol. 312, issue C

Abstract: The randomness and fluctuations in wind power generation present significant challenges for grid and wind farm dispatching. Accurate very short-term wind power forecasting (WPF) is therefore essential for the efficient operation of modern power systems. Data-driven models, such as Transformers, have demonstrated their effectiveness in WPF due to their ability to efficiently capture global features in long sequences. However, limited research has examined the impact of incorporating static data into WPF, which may limit forecasting accuracy. This paper proposes a Temporal Fusion Transformer forecasting model to address this challenge. This approach employs static data as the input features for the model. The model includes feature selection through a variable selection network and employs a specialized temporal fusion decoder to learn effectively from these static features. The case results show that the results of the proposed model are more accurate than the state-of-the-art methods, reducing MAPE by at least 1.32%, RMSE by 0.0091, and improving R2 by 0.035 in case studies. Additionally, the model maintains a manageable computational burden, underscoring its practical applicability.

Keywords: Wind power forecasting; Very short-term forecasting; Improved transformer; Static data; Temporal fusion decoder (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:312:y:2024:i:c:s0360544224033553

DOI: 10.1016/j.energy.2024.133577

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