Application of Steady State Data Compressed Sensing Based on LSTM and RNN in Rural Power Grid

With the advancement of intelligent power systems and the upgrading of rural power grids, steady-state signal compression technology for rural power grids has been widely applied. The use of neural network models for encoding and compressing steady-state signals in rural power grids is currently a commonly used data compression method. This article analyzes data compression methods based on LSTM and RNN models, and compares their compression effects. The results show that RNN based models can better achieve data compression while maintaining high data accuracy.