Air-conditioning energy consumption model training method and air-conditioning system control method

Abstract

The present application relates to a training method for an energy consumption model of an air-conditioning system and a control method, device, computer equipment and storage medium for the air-conditioning system, wherein the method includes: constructing energy consumption mechanism models of each component device respectively according to input parameters and output parameters of the air-conditioning system , train the energy consumption mechanism model based on the historical operation data, build the energy consumption model of each component equipment respectively, determine the control quantity constraints and state quantity constraints of the air conditioning system, and solve the problem by genetic algorithm and gradient optimization algorithm within a reasonable range of control quantities The optimal variables in the energy consumption model are obtained to obtain the control optimization strategy of the air-conditioning system. In the whole process, within the effective boundary range of environmental quantity constraints and control quantity constraints, the optimal variables based on genetic algorithm and gradient optimization algorithm are respectively calculated for the energy consumption model of the equipment in the air-conditioning system, which can accurately obtain the energy consumption under the current environment. The control strategy of the air-conditioning system with the least consumption can realize the good energy-saving effect of the air-conditioning system.

Description

technical field [0001] The present application relates to the technical field of air conditioning systems, and in particular, to a training method for an energy consumption model of an air conditioning system and a control method, device, computer equipment and storage medium for an air conditioning system. Background technique [0002] Air conditioning systems are currently widely used, and most of the scenarios such as public buildings, commercial buildings, and data centers use this system to achieve functions such as temperature control and dehumidification. Large-scale applications also bring huge energy consumption. Take data centers as an example. As of 2019, data center energy consumption accounts for more than 1.3% of global energy consumption, of which air-conditioning system energy consumption accounts for more than 40%, while commercial energy consumption accounts for more than 40%. Buildings and public buildings have a higher proportion of energy consumption. ...

AI Technical Summary

Problems solved by technology

[0004] However, in the above-mentioned control process, the specific setting of the temperature difference and the setting of the chilled water outlet temperature are actually determined according to manual experience, and cannot be adjusted adaptively according to the indoor and outdoor environments.

For the building system, its environmental quantity will undoubtedly change. The temperature, humidity, load, etc. all change with time. The fixed value given manually cannot change according to the perception of the environmental quantity, and it is impossible to realize the best performance for the air conditioning system. energy-saving control

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