Harnessing smart meter data for a Multitiered Energy Management Performance Indicators (MEMPI) framework: A facility manager informed approach

Energy management information systems (EMIS) play a critical role in providing actionable insights into building operations, timely feedback, and—ultimately—large energy savings. Current EMIS technologies often focus on industrial applications or require large upfront investments and trained operators, therefore greatly limiting its penetration into existing buildings. This paper integrates methods from social, building, and data sciences to understand limitations of current EMIS systems and inform the design of a new Multitiered Energy Management Performance Indicators (MEMPI) framework for characterizing the energy performance of buildings. Specifically, we employed a mixed methods research approach in which we first conduct in-depth qualitative interviews of 10 facility managers and energy consultants. We utilize the insights from our interviews to inform the design of the MEMPI framework, which harnesses highly granular data from already installed advanced metering infrastructure (AMI) (i.e., smart meters). The MEMPI framework employs quantile regression to first benchmark the energy performance of buildings to each other and generate key performance indicators (KPIs). We apply the MEMPI framework to real data from 569 public school buildings in California and measure their energy performance across multiple time scales (e.g., daily, monthly, yearly). Finally, we conduct case studies to compare insights from the MEMPI framework to the perceptions of facility managers overseeing 8 schools through a mixed methods qualitative and quantitative post-interview survey. Results from the case study show that facility managers’ perceptions of the performance of their schools were largely accurate, yet the poor energy performance from certain pieces of building equipment and operating schedules was overlooked by building managers. Overall, the MEMPI framework aims to bridge the gap between data-driven energy management models and qualitative domain knowledge held by facility managers to provide more comprehensive insights into the energy performance of buildings.