The Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications
- Rocky Mountain Inst., Boulder, CO (United States)
- Univ. of Chicago, IL (United States)
- DNV GL, Oslo (Norway)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- EnergySavvy, Seattle, WA (United States)
- U.S. Department of Energy (DOE), Baltimore, MD (United States)
- Pacific Gas and Electric, San Francisco, CA (United States)
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. Using M&V methods that capture load shapes together with automated processing1 can determine savings in near-real time to provide stakeholders with more timely and detailed information. This information can be used to inform ongoing building operations, provide early input on energy efficiency program design, or assess the impact of efficiency by location and time of day. Stakeholders who can make use of such information include regulators, energy efficiency program administrators, program evaluators, contractors and aggregators, building owners, the investment community, and grid planners. Although each stakeholder has its own priorities and challenges related to savings measurement and verification, the potential exists for all to draw from a single set of efficiency valuation data. Such an integrated approach could provide a base consistency across stakeholder uses.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- DOE Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1350974
- Report Number(s):
- LBNL-1007125; ir:1007125
- Country of Publication:
- United States
- Language:
- English
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