Multi-modal transformer-based wind power prediction by utilizing heterogeneous sources

Hongxing Han; Xinyu Liang; Kailin Zhu

doi:10.1117/12.3024579

13 May 2024 Multi-modal transformer-based wind power prediction by utilizing heterogeneous sources

Hongxing Han, Xinyu Liang, Kailin Zhu

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131591H (2024) https://doi.org/10.1117/12.3024579
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

The increasing global energy shortage necessitates a swift transition to renewable energy sources, with wind power emerging as a cost-effective and environmentally friendly option. However, the non-stationary, random, and intermittent nature of wind poses challenges for power grid management, leading to inefficiency and energy supply-demand imbalances. To address these issues, various computational methods have been developed, including multi-modal machine learning methods. However, existing ANN or LSTM-based methods fail to learn complex patterns from heterogeneous features efficiently, especially when NWP forecast map data is considered. Thus, this paper presents a multi-modal transformer-based deep learning approach for wind power prediction, utilizing transformer model and vision transformer model to handle both historical turbine-level time-series data and NWP forecast map data. Extensive experiments demonstrate the model's ability to handle heterogeneous features effectively, outperforming benchmark algorithms in wind power prediction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongxing Han, Xinyu Liang, and Kailin Zhu "Multi-modal transformer-based wind power prediction by utilizing heterogeneous sources", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131591H (13 May 2024); https://doi.org/10.1117/12.3024579

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