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<p>This study uses Recurrent Neural Network (RNN) model and Electricity Consumption (EC) management model to conduct in-depth research and analysis to achieve effective management of peak shifting EC in smart grids and analyze its influencing factors. A management method that is superior to other models has been found, which has a breakthrough effect on the use and research of smart grids. Firstly, the RNN model is analyzed, and a smart grid energy consumption prediction model based on EC management of peak shifting is established. Secondly, with the goal of shifting peaks and filling valleys, the power load data with several different characteristics is divided into different EC modes. Finally, in predicting smart grid energy consumption, it is compared with Linear Regression (LR), nonlinear regression, and Autoregressive Integrated Moving Average Mode prediction models. The results show that: (1) Before using the hydrogen energy peak shifting and valley filling EC management mode, the peak energy consumption is about 46kWh, and the adjusted peak energy consumption decreases by about 12kWh. This indicates that the hydrogen peak shifting EC management mode operates well in smart grids. (2) In practical environments, the Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of the RNN-based energy consumption time series prediction model are 0.124, 0.26, and 5.75, respectively. The MSE, MAE, and MAPE values of the LR prediction model are 22.09, 3.33, and 21.48, respectively. The MSE, MAE, and MAPE values of the nonlinear regression prediction model are 0.223, 489, and 16.32, respectively, indicating that the prediction accuracy of the RNN energy consumption time series prediction model is superior to other comparative models. This study has important reference value for schools and factories to use artificial intelligence to elucidate future energy consumption needs and reasonable allocation of energy in the power grid.</p>

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The peak shifting electricity consumption management and influencing factors of smart grid from recurrent neural network model and deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Feng Wang,
  • Dingwei Huang,
  • Weitian Lu

摘要

This study uses Recurrent Neural Network (RNN) model and Electricity Consumption (EC) management model to conduct in-depth research and analysis to achieve effective management of peak shifting EC in smart grids and analyze its influencing factors. A management method that is superior to other models has been found, which has a breakthrough effect on the use and research of smart grids. Firstly, the RNN model is analyzed, and a smart grid energy consumption prediction model based on EC management of peak shifting is established. Secondly, with the goal of shifting peaks and filling valleys, the power load data with several different characteristics is divided into different EC modes. Finally, in predicting smart grid energy consumption, it is compared with Linear Regression (LR), nonlinear regression, and Autoregressive Integrated Moving Average Mode prediction models. The results show that: (1) Before using the hydrogen energy peak shifting and valley filling EC management mode, the peak energy consumption is about 46kWh, and the adjusted peak energy consumption decreases by about 12kWh. This indicates that the hydrogen peak shifting EC management mode operates well in smart grids. (2) In practical environments, the Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of the RNN-based energy consumption time series prediction model are 0.124, 0.26, and 5.75, respectively. The MSE, MAE, and MAPE values of the LR prediction model are 22.09, 3.33, and 21.48, respectively. The MSE, MAE, and MAPE values of the nonlinear regression prediction model are 0.223, 489, and 16.32, respectively, indicating that the prediction accuracy of the RNN energy consumption time series prediction model is superior to other comparative models. This study has important reference value for schools and factories to use artificial intelligence to elucidate future energy consumption needs and reasonable allocation of energy in the power grid.