The Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications

The Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications

TitleThe Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications
Publication TypeReport
Year of Publication2017
AuthorsEllen M Franconi, Matt Gee, Miriam L Goldberg, Jessica Granderson, Tim Guiterman, Michael Li, Brian Arthur Smith
Date Published02/2017
InstitutionLawrence Berkeley National Laboratory
CityBerkeley
KeywordsM&V, M&V2.0
Abstract

Advanced measurement and verification (M&V) of energy efficiency savings, often referred to as M&V 2.0 or advanced M&V, is currently an object of much industry attention. Thus far, however, there has been a lack of clarity about what techniques M&V 2.0 includes, how those techniques differ from traditional approaches, what the key considerations are for their use, and what value propositions M&V 2.0 presents to different stakeholders.The objective of this paper is to provide background information and frame key discussion points related to advanced M&V. The paper identifies the benefits, methods, and requirements of advanced M&V and outlines key technical issues for applying these methods. It presents an overview of the distinguishing elements of M&V 2.0 tools and of how the industry is addressing needs for tool testing, consistency, and standardization, and it identifies opportunities for collaboration.In this paper, we consider two key features of M&V 2.0: (1) automated analytics that can provide ongoing, near-real-time savings estimates, and (2) increased data granularity in terms of frequency, volume, or end-use detail. Greater data granularity for large numbers of customers, such as that derived from comprehensive implementation of advanced metering infrastructure (AMI) systems, leads to very large data volumes. This drives interest in automated processing systems. It is worth noting, however, that automated processing can provide value even when applied to less granular data, such as monthly consumption data series. Likewise, more granular data, such as interval or end-use data, delivers value with or without automated processing, provided the processing is manageable. But it is the combination of greater data detail with automated processing that offers the greatest opportunity for value.

LBNL Report Number

LBNL-1007125