Predictive Analysis of Direct Air-Cooling Freeze Protection Based on Data Mining

This research presents an innovative optimization approach that combines data mining and deep learning techniques to enhance anti-freezing protection in direct aircooled thermoelectric systems operating in China’s cold regions. The main factors affecting the condensate temperature are analysed by establishing the heat balance equation and the cold end mechanism model. The study uses a long and short-term memory network model to predict the condensate temperature, and the results show that the two-layer model is better than the single-layer model in terms of prediction accuracy. The study shows that the method can effectively monitor and optimise the cold-end operation, providing theoretical support and technical guarantee for improving the system performance.