Online monitoring method of electric power enterprise operation state based on data driving

Traditional methods of monitoring and controlling power enterprises are difficult to deal effectively with complex dynamic systems due to limitations such as inaccuracy, lack of real-time performance, and inability to adapt to system changes. To overcome these shortcomings, we propose a data-driven online monitoring method, based on the long-term and short-term memory network (LSTM) and self attention mechanism to build an intelligent real-time monitoring system. The model uses multiple regression analysis to screen indicators, double-layer LSTM to learn the relationship between indicators, and the attention layer to enhance the processing ability of time series data. The data set of a power plant in Guangdong is used in the experiment, and the short-term power load is taken as the target. The experimental results show that the precision rate of the model in the prediction task is 93.14%, the recall rate is 91.44%, and the F1 score is 92.64%. This method improves the prediction accuracy and shows value in actual operation. Future research directions include introducing more complex deep learning models, integrating external factor data and developing adaptive algorithms. This study provides innovative ideas and practical tools for the intelligent transformation of electric power enterprise.