Grid Data Analysis and Load Forecasting Model Based on Federated Learning Technology and LSTM Algorithm

Abstract

With the continuous improvement of the national economy, the development of power enterprises is gradually accelerating, and the popularity of smart grids is also increasing. The power grid data center contains a large amount of user data, and analyzing this data can help power companies predict the load of power plants, thereby improving the resource utilization efficiency of power enterprises. However, current load forecasting models still suffer from information leakage and inaccurate predictions during data transmission, storage, and analysis processes. To solve the above problems, this study uses federated learning technology to optimize the long short-term memory network algorithm and analyzes power grid data and load forecasting based on the optimized algorithm. This study first conducted comparative experiments on the optimized algorithm and found that the prediction accuracy of the optimized algorithm reached 94.5%, with a prediction time of only 1.2ms. The analysis of the data using a load forecasting model based on this algorithm showed that the data security of the model has been improved by 23.4%. After using this model, the power company's electricity resource utilization rate increased by 31.8% and operating costs decreased by 27.5%. The proposed power grid data analysis and load forecasting model can ensure the privacy of power grid data and improve prediction accuracy, thereby improving the power grid operation efficiency of power enterprises and optimizing enterprise resource allocation.