Thermal energy storage thermal data processing for heating systems

Abstract

In order to solve the problem that the traditional industrial control methods cannot control the heating flow and water temperature in a timely and effective manner due to the high delay and complex coupling characteristics of the urban central heating system, the authors propose deep learning-based data processing and management for thermal heating systems. The author analyzes the non-ideality of district heating system and its influence on the application of deep learning technology, and gives solutions, respectively, finally, a primary side regulation scheme of district heating system based on deep learning and automatic control technology is proposed as a whole. The experimental results show that, by comparing the water supply temperature predicted by the equipment model of the primary side heat station with its actual measured value, the mean square error of the prediction results using the model directly is 1.30%, and the mean square error after model correction is 0.094%. The secondary return water temperature was controlled by adjusting the opening of the primary side electric valve, the expected secondary return water temperature in the scheme was compared with the actual secondary return water temperature, and the mean square error was 0.102%. It is proved that the scheme can achieve good control effect in the actual system, and the data result proves that the scheme is feasible.