TY - JOUR
TI - Thermal energy storage thermal data processing for heating systems
AU - Luo Cenhong
JN - Thermal Science
PY - 2023
VL - 27
IS - 2
SP - 1133
EP - 1140
PT - Article
AB - 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.