OFFSHORE WIND POWER PREDICTION BASED ON IMPROVED DENOISING AUTO-ENCODER AND MULTIVARIATE TIME SERIES CLUSTERING

Zhou Hai; Liu Jianfeng; Zhou Jian; Zhou Yongliang; Li Meiyu; Li Chenyang

doi:10.19912/j.0254-0096.tynxb.2021-1240

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (3) : 129-138. DOI: 10.19912/j.0254-0096.tynxb.2021-1240

OFFSHORE WIND POWER PREDICTION BASED ON IMPROVED DENOISING AUTO-ENCODER AND MULTIVARIATE TIME SERIES CLUSTERING

Zhou Hai¹, Liu Jianfeng¹, Zhou Jian², Zhou Yongliang³, Li Meiyu³, Li Chenyang⁴

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Abstract

Aiming at the characteristics of low precision and complex meteorological factors of offshore numerical weather prediction (NWP), this paper proposes a short-term offshore wind power forecasting method based on improved bi-directional denoising auto-encoder (BDAE) and clustering of multivariate time series. Firstly, toeplitz inverse Covariance-based clustering (TICC) is used to classify the similarity of wind conditions, that is, the multivariate series are segmented and clustered in real time according to the wind speed of offshore NWP at 30, 70, and 100 m. Secondly, according to different wind conditions, an improved BDAE correction model which can extract the past and future bidirectional effective information is established to correct the prediction error of hub height wind speed.Finally, based on the modified hub height wind speed and other NWP data, the TICC algorithm is used to classify meteorological similarity types, and the corresponding offshore wind power prediction model is established on this basis. The experimental verification is carried out with the data of an offshore wind farm in China, and the results show that the proposed method can improve the accuracy of offshore wind powerforecasting, which have certain engineering practical value.

Key words

offshore wind farms / weather forecasting / clustering algorithms / wind power forecasting / improved bi-directional denoising auto-encoder / multivariate time series

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Zhou Hai, Liu Jianfeng, Zhou Jian, Zhou Yongliang, Li Meiyu, Li Chenyang. OFFSHORE WIND POWER PREDICTION BASED ON IMPROVED DENOISING AUTO-ENCODER AND MULTIVARIATE TIME SERIES CLUSTERING[J]. Acta Energiae Solaris Sinica. 2023, 44(3): 129-138 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1240

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