LSTM Neural Network Intelligent Power Load Based on TensorFlow

With the growth of global energy demand and the widespread use of renewable energy, how to accurately predict power demand has become a key issue to ensure the stability of the power grid and optimize energy distribution. The application of TensorFlow-based LSTM neural network in intelligent power load research mainly focuses on improving the accuracy and efficiency of power demand prediction. In this study, the LSTM model based on the TensorFlow framework is used to predict power demand. Through training on historical load data, the model learns the timing characteristics of power demand in order to achieve more accurate predictions. The research results show that the accuracy rate of the LSTM model based on the TensorFlow framework is above 94.15%, and the highest accuracy rate is 95.78%. Experimental data show that the system significantly improves the accuracy of prediction. The LSTM model has wide application potential in the future development of smart grids and power systems, and can provide strong technical support for the reliable operation of the power grid and the efficient use of energy.