WIND SPEED PREDICTION BASED ON MULTIPLE FEATURE EXTRACTION AND TRANSFER LEARNING

Liang Tao; Chen Chunyu; Tan Jianxin; Jing Yanwei

doi:10.19912/j.0254-0096.tynxb.2021-1535

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (4) : 132-139. DOI: 10.19912/j.0254-0096.tynxb.2021-1535

WIND SPEED PREDICTION BASED ON MULTIPLE FEATURE EXTRACTION AND TRANSFER LEARNING

Liang Tao¹, Chen Chunyu¹, Tan Jianxin², Jing Yanwei²

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Abstract

In order to meet the requirements of remote control centers for efficient and low-cost wind speed prediction of wind farms at different locations, this paper proposes a multivariable wind speed prediction model based on multiple feature extraction and transfer learning by combining the idea of "offline training, online prediction". The offline model fuses wind speed information captured by two-channel convolutional neural network and bi-directional long-short-term memory neural network. Wind speed characteristics of wind farms at typical locations are learned, and then the wind speed characteristics are transferred to other wind farms to achieve online prediction. The prediction accuracy is further improved by using an improved multi-objective grasshopper optimization algorithm, which integrates the prediction results of each typical wind farm. Finally, the superiority of the model is verified by the data of a centralized control center in Hebei. The results show that the adaptability and accuracy of the proposed model are superior than that of other baseline models.

Key words

wind energy / wind speed prediction / feature extraction / convolutional neural network / bi-directional long short-term memory network / transfer learning / multi-objective grasshopper optimization algorithm

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Liang Tao, Chen Chunyu, Tan Jianxin, Jing Yanwei. WIND SPEED PREDICTION BASED ON MULTIPLE FEATURE EXTRACTION AND TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 132-139 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1535

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