A novel ultra-short-term wind power prediction method based on XA mechanism

Cheng Peng, Yiqin Zhang, Bowen Zhang, Dan Song, Yi Lyu and AhChung Tsoi

Applied Energy, 2023, vol. 351, issue C, No S0306261923012692

Abstract: A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset.

Keywords: Deep convolutional neural network; Wind power; Power prediction; Long short-term memory network; Time series; Cross attention (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2023.121905

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