Benchmarking Lab Performance: Where Are We Today?

Craig Wray, Lawrence Berkeley National Laboratory

Laboratories are one of the most energy intensive areas in buildings, due in part to a myriad of health and safety requirements that significantly affect energy use. To identify opportunities for performance improvement, lab owners and operators can use the benchmarking process to compare the performance of their lab to similar facilities.

In this presentation, we discuss the whole building energy metrics available in the Labs21 Energy Benchmarking Tool, which contains information from more than 900 lab facilities. We then present empirical benchmarking methods based on simple data filtering (as provided to tool users) as well as a multivariate regression analysis of the Labs21 database. Of particular note, the regression analysis showed that lab type, lab-area ratio, and occupancy hours are significant variables. However, the dataset did not allow analysis of important factors such as air change rates or hazard classifications, because those data are not currently collected via the Labs21 tool. For comparison, we also discuss how a simulation-based method can generate a benchmark energy intensity normalized for a wider range of parameters. We suggest that both methods have complementary strengths and limitations and propose a hybrid benchmarking approach.

Finally, we describe how action-oriented benchmarking utilizing system level metrics such as Watts/cfm for ventilation can be used to identify more specific potential improvements. While such benchmarking is not an audit in a box and is not intended to provide the same degree of accuracy as an on-site audit, we demonstrate how it can be used to focus and prioritize audit activity and to track performance at the system level.

Learning Objectives

  • Identify which whole building energy metrics are currently available in the Labs21 benchmarking tool.
  • Explain how to use the tool and the benefits/limitations of data analyses involving filtering and regression of empirical benchmark data.
  • Discuss how simulation-based benchmarking can supplement empirical data.
  • Describe how system-level data can be used for 'action-oriented' benchmarking.

Biography:

Craig Wray has over 30 years of experience as a consulting engineer and scientist addressing energy, airflow, pollutant transport, and commissioning issues in buildings. At Berkeley Lab, his current efforts focus on air-handling system experiments, modeling these systems and their interactions with building components, developing related diagnostic methods, and assessing the benefits and risks of improvements. He leads the Laboratory Technology Solutions Team for DOE's Better Buildings Alliance.

 

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