Short-term wind power forecasting model based on temporal convolutional network and Informer

Mingju Gong, Changcheng Yan, Wei Xu, Zhixuan Zhao, Wenxiang Li, Yan Liu and Sheng Li

Energy, 2023, vol. 283, issue C

Abstract: Wind power forecast remains challenging owing to the unpredictable peculiarity of wind. The accuracy of wind power predictions is critical to the stability of the whole system. This research proposes a hybrid prediction model based on a temporal convolutional network and an Informer to increase the accuracy of wind power forecasting. The hidden temporal features in the dataset are first extracted using TCN, and the Informer is then employed to predict wind power. Additionally, a cutting-edge AdaBelief optimizer is used to boost prediction accuracy even more. The validity of the model is verified by comparing with other wind speed prediction methods. The findings reveal that the proposed model has the highest prediction accuracy and the best forecast effect.

Keywords: Informer; Wind power forecasting; Feature extraction; Temporal convolution network (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025653

DOI: 10.1016/j.energy.2023.129171

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