Hybrid Deep Learning Model for Short-Term Wind Speed Forecasting Based on Time Series Decomposition and Gated Recurrent Unit

Accurate wind speed prediction has been becoming an indispensable technology in system security, wind energy utilization, and power grid dispatching in recent years. However, it is an arduous task to predict wind speed due to its variable and random characteristics. For the objective to enhance the...

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Бібліографічні деталі
Автори:	Changtong Wang, Zhaohua Liu, Hualiang Wei, Lei Chen, Hongqiang Zhang
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Tsinghua University Press 2021-12-01
Серія:	Complex System Modeling and Simulation
Предмети:	deep learning complete ensemble empirical mode decomposition with adaptive noise (ceemdan) gated recurrent unit (gru) short term wavelet packet decomposition wind speed prediction
Онлайн доступ:	https://www.sciopen.com/article/10.23919/CSMS.2021.0026
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Hybrid Deep Learning Model for Short-Term Wind Speed Forecasting Based on Time Series Decomposition and Gated Recurrent Unit

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