Research on the room air conditioner long-term performance prediction and optimization strategy

Abstract

Performance of occupied room air-conditioner (RAC) is an important evaluation index to estimate RAC continue energy saving efficiency. In order to investigate characteristic of RAC long-term performance (LTP) and acquire the cost optimation design methodology of high LTP in multi-factors impact condition, a BP neural network prediction method has been applied. The training sample of LTP prediction BP neural network acquired form experimental result of occupied RACs and data of RACs dynamic LTP on-line monitor system. By a large size of training sample, the decision weights of multi-impact factors and LTP optimation strategies can be obtained. The performances of 26 occupied RACs have also been tested. 85% of testing data ias served as training sample data and 15% of testing data ias served as validation data to LTP prediction BP neural network. The result indicated that the prediction is convergence and error is less than 5% during the BP neural network training by 22 samples. The decision weights of time weighted high temperature cooling, rated cooling, low temperature heating, rated heating normalized performance value are 0.187, 0.203, 0.312, 0.298, respectively. For further increasing the prediction precision, RAC performance online monitor system and LTP online data acquisition website has been established for data acquisition to validate LTP prediction BP neural network. Based on the acquisition database, a big data mining method has also been proposed in RAC LTP optimization design and investigation.