  Energy usage monitoring on higher education campuses is an important step for
providing satisfactory service, lowering costs and supporting the move to green
energy. We present a collaboration between the Department of Statistics and
Facilities Operations at an R1 research university to develop statistically
based approaches for monitoring monthly energy usage and proportional yearly
usage for several hundred utility accounts on campus. We compare the
interpretability and power of model-free and model-based methods for detection
of anomalous energy usage patterns in statistically similar groups of accounts.
Ongoing conversation between the academic and operations teams enhances the
practical utility of the project and enables implementation for the university.
Our work highlights an application of thoughtful and continuing collaborative
analysis using easy-to-understand statistical principles for real-world
deployment.
