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.