A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction

Zhijian Qu, Jian Li, Xinxing Hou and Jianglin Gui

Energy, 2023, vol. 281, issue C

Abstract: The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlation characteristics of wind-farm clusters. The Bagging, long short-term memory (LSTM), and random forest (RF) are superimposed based on the stacking integration algorithm. By analyzing the decomposed wind electron sequence characteristics, two Stacking models are used to predict the wind power of target wind-farms respectively and then a dual stacking model fusion strategy is formed. Finally, a multi-step prediction model with the spatio-temporal characteristics of D-stacking multi-integration fusion is designed for rolling multi-step prediction of wind-farm cluster power to obtain high-precision target wind-farm power. Through the actual wind power generation measured in northwest China to conduct case studies and comparative tests, it concludes that：(1) Spatio-temporal method in this paper can effectively extract the deep spatial features of wind farm clusters. (2) Dual fusion strategy improves Stacking effectively. (3) The proposed model can obtain accurate wind power prediction results, which is superior to 14 comparative algorithms proposed by other researchers.

Keywords: Spatio-temporal graph deep neural network; Improved stacking; Multi-integration fusion; Wind power forecasting; Multi-step rolling forecast (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:281:y:2023:i:c:s0360544223016833

DOI: 10.1016/j.energy.2023.128289

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