Deep learning methods for smart grid data analysis

This study aims to explore the application of deep learning technology in smart grid data analysis to improve the efficiency and reliability of power grid operation. By using LSTM (LSTM) and convolutional neural network (CNN), this paper analyzes the performance of these models in power load forecasting, fault detection and prevention, and energy distribution optimization in detail. Research results show that deep learning technology can significantly improve the accuracy of power demand forecasting from $70 \%$ to $\mathbf{8 5 \%}$, reduce fault response time, and improve fault detection accuracy to $\mathbf{1 0 0 \%}$. In addition, the optimized energy distribution strategy significantly reduces operating costs and energy waste. The research conclusion shows that deep learning not only optimizes the operating efficiency of the power grid, but also enhances the economy and sustainability of the power grid, confirming its practical value and broad application potential in smart grid management.