TY - JOUR
TI - Thermal station modelling and optimal control based on deep learning
AU - Cao Min
JN - Thermal Science
PY - 2021
VL - 25
IS - 4
SP - 2965
EP - 2973
PT - Article
AB - To solve the mismatch between heating quantity and demand of thermal  stations, an optimized control method based on depth deterministic strategy  gradient was proposed in this paper. In this paper, long short-time memory  deep learning algorithm is used to model the thermal power station, and  then the depth deterministic strategy gradient control algorithm is used to  solve the water supply flow sequence of the primary side of the thermal  power station in combination with the operation mechanism of the central  heating system. In this paper, a large number of historical working  condition data of a thermal station are used to carry out simulation  experiment, and the results show that the method is effective, which can  realize the on-demand heating of the thermal station a certain extent and  improve the utilization rate of heat.