Detecting the undetected: Dealing with non-routine events using advanced M&V meter-based savings approaches

Author(s): Fernandes, Samuel; Crowe, eliot; Touzani, samir; Granderson, jessica | Abstract: In a rapidly evolving energy industry, utilities are dealing with new challenges like integrating distributed energy resources and market saturation for advanced lighting retrofits. Demand-side management programs require new approaches to meet aggressive carbon reduction goals. Advanced measurement a verification (MaV) is an energy data analysis method using smart meter data in combination with analytics to quantify energy efficiency project savings. Advanced MaV shows great promise for supporting next generation commercial programs including retro commissioning, multi-measure retrofits, and behavior change programs. Advanced MaV captures real project impacts at the meter, but sometimes non-project events can also impact consumption (so-called “non-routine events” [NREs]). Accurately detecting and accounting for NREs is important for reducing uncertainty of savings estimates and helps manage investment risk for different stakeholders (e.g., utilities, building owners, ESCOs). Recent research has shown promise in establishing data-driven techniques to identify and adjust for NREs, but fundamental questions still remain, such as: how can you distinguish NREs from acceptable noise in energy consumption profiles? What is the frequency and magnitude of NREs? Can their detection and adjustment be automated and streamlined? This paper documents the state of the art in NRE quantification and analysis. The results of research to quantify the frequency, nature and direction of NREs, and methods and metrics for determining a trigger threshold for taking action on NREs are presented. The paper also documents the latest technical guidance on application of NRE detection and adjustment methods.