A statistical approach to thermal zone mapping

Although in most buildings the spatial allocation of cooling resources can be managed using multiple air handling units and an air ducting system, it can be challenging for an operator to leverage this capability, partially because of the complex interdependencies between the different control options. This is in particular important for data centers, where cooling is a major cost while the sufficient allocation of cooling resources has to ensure the reliable operation of mission-critical information processing equipment. It has been shown that thermal zones can provide valuable decision support for optimizing cooling. Such Thermal zones are generally defined as the region of influence of a particular cooling unit or cooling "source" (such as an air condition unit (ACU)). In this paper we show results using a statistical approach, where we leverage real-time sensor data to obtain thermal zones in realtime. Specifically, we model the correlations between temperatures observed from sensors located at the discharge of an ACU and the other sensors located in the room. Outputs from the statistical solution can be used to optimize the placement of equipment in a data center, investigate failure scenarios, and make sure that a proper cooling solution has been achieved.