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

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 correlatio...

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Bibliographic Details
Published in:	Energy (Oxford) Vol. 281; p. 128289
Main Authors:	Qu, Zhijian, Li, Jian, Hou, Xinxing, Gui, Jianglin
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.10.2023
Subjects:	algorithms China energy Improved stacking Multi-integration fusion Multi-step rolling forecast neural networks power generation prediction Spatio-temporal graph deep neural network uncertainty wind wind farms wind power Wind power forecasting China Improved stacking Spatio-temporal graph deep neural network Multi-integration fusion Multi-step rolling forecast Wind power forecasting
ISSN:	0360-5442
Online Access:	Get full text
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