Quantitative relation between energy consumption and key air conditioning system parameters in data centers based on data mining method

Uses Lasso regression screening and manual parameter screening to filtrate the input parameters of the energy consumption prediction model of data centers. Implements the XGBoost algorithm to do data mining about the energy consumption and air conditioning system parameters of a data center in Beijing. Achieves the accurate prediction of the power usage effectiveness (PUE) of the data center. Observes the quantitative influence correlation rank of the air conditioning system parameters on the PUE. Verifies the reliability of the parameter selection by the data mining method.