WIND DIRECTION PREDICTION ALGORITHM BASED ON CEEMDAN AND TIMPORAL CONVOLUTIONAL NETWORK

Zhang Qun; Hou Yuqiang; Xu Jianbing; Zhao Wei; Li Wei; Liu Fusuo

doi:10.19912/j.0254-0096.tynxb.2023-0928

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 512-520. DOI: 10.19912/j.0254-0096.tynxb.2023-0928

WIND DIRECTION PREDICTION ALGORITHM BASED ON CEEMDAN AND TIMPORAL CONVOLUTIONAL NETWORK

Zhang Qun, Hou Yuqiang, Xu Jianbing, Zhao Wei, Li Wei, Liu Fusuo

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Abstract

In order to improve the accuracy of wind direction prediction, a combined prediction algorithm based on decision tree method （CART）, random forest algorithm, complete adaptive noise empirical mode decomposition （CEEMDAN） and temporal convolutional network （TCN） is proposed. Among them, the input importance evaluation based on decision tree method is used to evaluate and screen the input relevance of wind direction perdiction models. Randon forest algorithm is used to classify and process wind direction data; The complete adaptive noise integrated empirical mode decomposition is used to decompose the input wind direction data and extract the input information features; Finally, the temporal convolutional network is used to build the wind direction prediction model. The experimental results show that compared to the other eight comparison models, the prediction errors of the proposed model for wind direction in the fourth quarter data set are less than 4.95°, and the highest prediction accuracy is achieved.

Key words

deep learning / feature extraction / random forest / temporal convolutional network / wind direction prediction

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Zhang Qun, Hou Yuqiang, Xu Jianbing, Zhao Wei, Li Wei, Liu Fusuo. WIND DIRECTION PREDICTION ALGORITHM BASED ON CEEMDAN AND TIMPORAL CONVOLUTIONAL NETWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 512-520 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0928

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