Lawrence Berkeley National Laboratory

Complex buildings such as laboratories, data centers and cleanrooms present particular challenges for energy benchmarking because it is difficult to normalize special requirements such as health and safety in laboratories and reliability (i.e., system redundancy to maintain uptime) in data centers which significantly impact energy use. For example, air change requirements vary widely based on the type of work being performed in each laboratory space. We present methods and tools for energy benchmarking in laboratories, as an exemplar of a complex building type. First, we address whole building energy metrics and normalization parameters. We present empirical methods based on simple data filtering as well as multivariate regression analysis on the Labs21 database. The regression analysis showed lab type, lab-area ratio and occupancy hours to be significant variables. Yet the dataset did not allow analysis of factors such as plug loads and air change rates, both of which are critical to lab energy use. The simulation-based method uses an EnergyPlus model to generate a benchmark energy intensity normalized for a wider range of parameters. We suggest that both these methods have complementary strengths and limitations. Second, we present "action-oriented" benchmarking, which extends whole-building benchmarking by utilizing system-level features and metrics such as airflow W/cfm to quickly identify a list of potential efficiency actions which can then be used as the basis for a more detailed audit. While action-oriented benchmarking is not an "audit in a box" and is not intended to provide the same degree of accuracy afforded by an energy audit, we demonstrate how it can be used to focus and prioritize audit activity and track performance at the system level. We conclude with key principles that are more broadly applicable to other complex building types.