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Combined adaptive DRN-GRU model based on ultra short-term PV generation forecasting
  • +2
  • Xiangyu Chen,
  • Guang Tian,
  • Jing Zhao,
  • Qingquan Jia,
  • jiaxin wan
Xiangyu Chen
Hebei Electric Power Co., LTD.
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Guang Tian
Hebei Electric Power Co., LTD.
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Jing Zhao
Hebei Vocational and Technical College of Communications
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Qingquan Jia
Yanshan University
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jiaxin wan
Yanshan University

Corresponding Author:[email protected]

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Abstract

Traditional deep neural network model training process will deepen the network to a certain number of layers will produce network degradation phenomenon, and then affect the training effect and prediction accuracy. To address this problem, a PV power prediction model based on deep residual network (DRN) and gated recurrent unit (GRU) neural network is proposed. Firstly, the Pearson correlation coefficient method is used to filter out the meteorological variables with high correlation with PV power from historical data and reduce the data dimensionality. Secondly, the DRN-GRU prediction model is proposed to be trained using the adaptive learning rate Adam optimization algorithm to obtain the optimal parameters. Finally, a DRN-GRU rolling prediction model is built based on the historical data series to derive the PV power prediction results. The results of the algorithm show that the model can still maintain good training effect in the network training of deep numbers, effectively solve the network degradation problem, and have higher prediction accuracy compared with models such as artificial neural networks and traditional convolutional neural networks.