中央空调系统的数据分析与最优控制策略

With the rapid popularization of central air conditioning, the energy consumption of central air conditioning is also a major problem to be solved urgently, and the optimization strategy of central air conditioning system is one of the most important solutions. In this paper, the measured data of a set of central air-conditioning system in a city are analyzed and processed deeply. BP neural network analysis is used to fit the relationship between three variables including system efficiency, power consumption and cooling load and other variables. The regression between the variables is carried out by using the ridge regression, and the relational model of the system efficiency and the uncontrollable variables is obtained and then optimized. The optimal control strategy is obtained by using the BP neural network to feedback the controllable variables.