Central air conditioner control method based on multi-agent deep reinforcement learning

Abstract

The invention discloses a central air-conditioning control method based on multi-agent deep reinforcement learning, and the method comprises the steps: carrying out the model-free optimization control of the starting and stopping states and working parameters of a cooler, a cooling water pump and a cooling water tower fan in a central air-conditioning system according to the current indoor demand cooling load and outdoor wet bulb temperature, including the operation sequence control of the cooler; according to the control method, an accurate central air-conditioning system model does not need to be established in the actual deployment process, the working frequency of the cooling water pump and the working frequency of the cooling water tower fan can be respectively controlled only by using a single agent, and the working frequency of the cooling water pump and the working frequency of the cooling water tower fan can be controlled by relying on a small amount of historical data. An efficient and accurate control strategy is trained in a short time, the unnecessary refrigerating capacity is reduced, the workload of a refrigerator, a cooling water pump and a cooling water tower fan is reduced, the service life is prolonged, the failure rate is reduced, and the energy consumption of the whole central air-conditioning system and even the total energy consumption of a building are greatly reduced.

Description

technical field [0001] The invention relates to the technical field of central air-conditioning control, in particular to a central air-conditioning control method based on multi-agent deep reinforcement learning. Background technique [0002] According to statistics, in the total energy consumption of buildings, the energy consumption of the central air-conditioning system accounts for even more than 50%, and the energy consumption of the chiller and cooling water system is an important part of the energy consumption of the central air-conditioning. The optimal control of the water system is particularly important to reduce the energy consumption of the entire central air-conditioning system and even the total energy consumption of the building. [0003] At present, in the control methods of the current central air-conditioning system, the optimal control methods mainly include rule-based control, model-based control and model-free control. Rule-based control is often stat...

AI Technical Summary

Problems solved by technology

The model-based method requires a large amount of historical data and sensor information to establish an accurate central air-conditioning model, but this method usually lacks good robustness and is not suitable for old buildings that lack historical data and sensors

In order to avoid the establishment of accurate mathematical models, model-free control methods have been adopted. Traditional model-free control methods need to discretize the state and action, resulting in a larger action space and longer training time. solving complex problems

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