华南理工大学 机械与汽车工程学院,广州 510641
School of Mechanical & Automotive Engineering,South China University of Technology,Guangzhou 510641,China
An energy-saving control strategy based on data-driven and self-learning mechanism is proposed to solve the problems of complex mechanism modeling, lack of self-learning ability and slow optimization speed of traditional energy-saving optimization methods for cold source systems. The Markov decision process model of cold source is designed and the deep deterministic policy gradient(DDPG) algorithm from policy gradient is used to solve the problem of dimensionality curse and can avoid discretization of control actions. In this paper, the central air conditioning cold source system of a large office building in the hot summer and warm winter area is selected as the research object, and the control strategy of the cold source system is optimized. The results show that under the premise of meeting the indoor thermal comfort requirement, the energy-saving control strategy of the system is realized with the goal of minimizing the energy consumption. In the comparison experiment, the total energy consumption of the cold source system under the DDPG control strategy is reduced by 6.47% and 14.42% compared with the PSO control strategy and the rule based control strategy, the average indoor thermal comfort is increased by 5.59% and 18.71%, and the proportion of total uncomfortable time is decreased by 5.22% and 76.70%, respectively. The simulation results show that the proposed control strategy has effectiveness and practicality, which has obvious advantage in energy-saving optimization compared with other control strategies.