Application of automated measurement and verification to utility energy efficiency program data [electronic resource].
- Berkeley, Calif. : Lawrence Berkeley National Laboratory, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 191-199 : digital, PDF file
- Additional Creators:
- Lawrence Berkeley National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Trustworthy savings calculations are critical to convincing regulators of both the cost-effectiveness of energy efficiency program investments and their ability to defer supply-side capital investments. Today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of energy efficiency programs. They also require time-consuming data acquisition. A spectrum of savings calculation approaches is used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data. The increasing availability of “smart” meters and devices that report near-real time data, combined with new analytical approaches to quantify savings, offers the potential to conduct M&V more quickly and at lower cost, with comparable or improved accuracy. Commercial energy management and information systems (EMIS) technologies are beginning to offer these ‘M&V 2.0’ capabilities, and program administrators want to understand how they might assist programs in quickly and accurately measuring energy savings. This paper presents the results of recent testing of the ability to use automation to streamline the M&V process. In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. For the data sets studied we evaluate the fraction of buildings that are well suited to automated baseline characterization, the uncertainty in gross savings that is due to M&V 2.0 tools’ model error, and indications of labor time savings, and how the automated savings results compare to prior, traditionally determined savings results. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14. In addition the findings suggest that M&V 2.0 methods may also offer time-savings relative to traditional approaches. Lastly, we discuss the implications of these findings relative to the potential evolution of M&V, and pilots currently being launched to test how M&V automation can be integrated into ratepayer-funded programs and professional implementation and evaluation practice.
- Report Numbers:
- E 1.99:lbnl--1007286
- Other Subject(s):
- Published through SciTech Connect.
Energy and Buildings 142 C ISSN 0378-7788 AM
Jessica Granderson; Samir Touzani; Samuel Fernandes; Cody Taylor.
Building Technology & Urban Systems
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