CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid

Abstract

Introduction: Smart grid (SG) technologies have a wide range of applications to improve the reliability, economics, and sustainability of power systems. Optimizing large-scale energy storage technologies for smart grids is an important topic in smart grid optimization. By predicting the historical load and electricity price of the power system, a reasonable optimization scheme can be proposed.Methods: Based on this, this paper proposes a prediction model combining a convolutional neural network (CNN) and gated recurrent unit (GRU) based on an attention mechanism to explore the optimization scheme of large-scale energy storage in a smart grid. The CNN model can extract spatial features, and the GRU model can effectively solve the gradient explosion problem in long-term forecasting. Its structure is simpler and faster than LSTM models with similar prediction accuracy. After the CNN-GRU extracts the data, the features are finally weighted by the attention module to improve the prediction performance of the model further. Then, we also compared different forecasting models.Results and Discussion: The results show that our model has better predictive performance and computational power, making an important contribution to developing large-scale energy storage optimization schemes for smart grids.