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Improved LSTM Spatial-temporal Prediction Method for Power Grid IoT Analysis

∗ Along with the development of IoT (Internet of Things), many sensors have been equipped in power grid system to monitor the status of the electricity infrastructure. It is necessary to predict the possible changes from recorded IoT data. In this paper, we propose an improved LSTM algorithm to analyze the spatial-temporal data from power grid IoT devices. Prophet-XGBoost algorithm is combined with LSTM algorithm to improve the prediction accuracy of the IoT datasets. The proposed method is implemented and compared with SVR, XGBoost, and general LSTM methods in different IoT datasets. The experimental results indicate that the proposed method can improve the prediction results and achieve better performance than these existing methods.