Research on Big Data Processing and Intelligent Analysis Algorithm in Electric Power Enterprise Information Platform

Traditional data processing methods can no longer meet current demands; this research focuses on constructing an efficient and reliable big data processing architecture, and exploring the application of intelligent analysis algorithms in power load forecasting. By adopting the Lambda architecture, combined with technologies such as Apache Hadoop and Apache Flink, the study achieves efficient processing of both historical and real-time data. Additionally, an LSTM (Long Short-Term Memory) model for power load forecasting is designed to capture long-term dependencies in time series data, significantly enhancing prediction accuracy. Case studies show that the LSTM model outperforms traditional methods, such as ARIMA and Support Vector Machines (SVM), in evaluation metrics like MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). Moreover, the application of the Lambda architecture has notably improved data processing efficiency and reduced operational costs. This research provides a scientific basis for the intelligent management of power enterprises and promotes sustainable development in the power industry.