A study of MLP-mixer with FFT for short term wind speed forecasting

Hailong Shu; Weiwei Song; Jiping Zhang; Zhen Song; Kaitao Xiao; Chaoqun Li

doi:10.1117/12.2656464

20 October 2022 A study of MLP-mixer with FFT for short term wind speed forecasting

Hailong Shu, Weiwei Song, Jiping Zhang, Zhen Song, Kaitao Xiao, Chaoqun Li

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245114 (2022) https://doi.org/10.1117/12.2656464
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

This paper proposes a prediction approach based on MLP-Mixer with FFT (The fast Fourier transformation). The wind speed series dataset was transformed using the FFT. Extract high dimensional features initially, then a deep learning time series prediction based on MLP mixer is introduced to explore and exploit the implicit information of wind speed time series for wind speed forecasting. We compared the different wind speed forcasting results by setting the lookback premeter to 4, 8, 12, and 16 hours. On the basis of two years of test dataset, the performance of the proposed FFT-MLP-mixer is effectively validated for short-term wind speed forecasting one hour in advance. The best wind speed prediction results are obtained when the lookback is 4, where the wind speed has an inflection point and the prediction results have slightly later features than the observation data.

Citation Download Citation

Hailong Shu, Weiwei Song, Jiping Zhang, Zhen Song, Kaitao Xiao, and Chaoqun Li "A study of MLP-mixer with FFT for short term wind speed forecasting", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245114 (20 October 2022); https://doi.org/10.1117/12.2656464

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