AUTHOR=Li Xuhan 

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

JOURNAL=Frontiers in Energy Research

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1228256

DOI=10.3389/fenrg.2023.1228256

ISSN=2296-598X

ABSTRACT=<p><bold>Introduction:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results and Discussion:</bold> 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.</p>