Recovery Model of Electric Power Data Based on RCNN-BiGRU Network Optimized by an Accelerated Adaptive Differential Evolution Algorithm

Yukun Xu, Yuwei Duan, Chang Liu, Zihan Xu and Xiangyong Kong ()
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Yukun Xu: Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China
Yuwei Duan: Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China
Chang Liu: Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China
Zihan Xu: Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China
Xiangyong Kong: School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China

Mathematics, 2024, vol. 12, issue 17, 1-26

Abstract: Time-of-use pricing of electric energy, as an important part of the national policy of energy conservation and emission reduction, requires accurate electric energy data as support. However, due to various reasons, the electric energy data are often missing. To address this thorny problem, this paper constructs a CNN and GRU-based recovery model (RCNN-BiGRU) for electric energy data by taking the missing data as the output and the historical data of the neighboring moments as the input. Firstly, a convolutional network with a residual structure is used to capture the local dependence and periodic patterns of the input data, and then a bidirectional GRU network utilizes the extracted potential features to model the temporal relationships of the data. Aiming at the difficult selection of network structure parameters and training process parameters, an accelerated adaptive differential evolution (AADE) algorithm is proposed to optimize the electrical energy data recovery model. The algorithm designs an accelerated mutation operator and at the same time adopts an adaptive strategy to set the two key parameters. A large amount of real grid data are selected as samples to train the network, and the comparison results verify that the proposed combined model outperforms the related CNN and GRU networks. The comparison experimental results with other optimization algorithms also show that the AADE algorithm proposed in this paper has better data recovery performance on the training set and significantly better performance on the test set.

Keywords: CNN; bidirectional GRU; differential evolution; adaptive parameter setting schemes; accelerated mutation operator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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