Deep learning compound trend prediction model for hydraulic turbine time series

Lei Xiong, Jiajun Liu, Bo Song, Jian Dang, Feng Yang and Haokun Lin

International Journal of Low-Carbon Technologies, 2021, vol. 16, issue 3, 725-731

Abstract: As a clean energy with mature technology, hydropower has been widely applied in industry. The hydraulic turbine unit plays an important role in hydropower station. Since the fault of turbine unit will affect the normal operation of the whole hydropower station, this paper proposes a universal, fast and memory-efficient method trend for time-series prediction of hydraulic turbines. The proposed method adopts the expressive power of deep neural networks and the time characteristics of sequence-to-sequence structure (parallel convolution and recurrent neural network) to make time-series prediction. It also uses convolutional quantile loss and memory network to predict future extreme events. The experimental results show that the proposed method is fast, robust and accurate. It can reduce at least 34% in mean square error and 33% in convergence speed compared with the existing methods.

Keywords: hydraulic turbine; time series prediction; deep neural networks; convolutional quantile loss; memory network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:16:y:2021:i:3:p:725-731.

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