The problem of high energy consumption and low energy efficiency of data center has been pard extensive attention to and investigated by researchers. However， there is no public dataset of server energy consumption for researchers to use， and current filter feature selection can not satisfy requirements of engineers. Here， a simulation environment architecture is proposed to simulate the running state of servers in the data center. Based on the proposed architecture， performance parameters and energy consumption data of server are collected when the server as running various tasks. Causal feature selection is applied to the feature analysis of energy consumption datasets， and thus an interpretable feature subset is constructed and the energy consumption forecast results are obtained. Experimental results show that the size of causal feature subset is about 1/3 to 1/6 of the size of filter feature subset， and the model trained with causal feature subset achieves the optimal prediction accuracy in 75% of the cases.