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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Vydané v:	Energy (Oxford) Ročník 281; s. 128289
Hlavní autori:	Qu, Zhijian, Li, Jian, Hou, Xinxing, Gui, Jianglin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.10.2023
Predmet:	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
On-line prístup:	Získať plný text
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