Energy-Efficient Data-Based Zonal Control of Temperature for Data Centers

In this work we address the problem of optimal economic thermal operation of data centers utilizing economic model predictive control. First, a data driven predictive zonal model is trained using subspace identification. This model is utilized in a model predictive controller to predict dynamic behavior of a data center. An economic model predictive control is designed to maintain temperature of each zone within an allowable range while minimizing operational costs of the cooling system. The effectiveness of the proposed method is illustrated through simulations on a mechanistic data center model.