|Title||Assessment of Automated Measurement and Verification (M&V) Methods|
|Year of Publication||2015|
|Authors||Jessica Granderson, Samir Touzani, Claudine Y Custodio, Michael D Sohn, Samuel Fernandes, David A Jump|
This report documents the application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-building energy savings.
Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today's methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming data acquisition and often do not deliver results until years after the program period has ended. A spectrum of savings calculation approaches are used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data.
The rising availability of "smart" meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. Energy management and information systems (EMIS) technologies, not only enable significant site energy savings, but are also beginning to offer M&V capabilities. This paper expands recent analyses [Price et al. 2013; Granderson and Price 2014; J. Granderson et al. 2015] of public-domain whole-building M&V methods, focusing on more novel baseline modeling approaches that leverage interval meter data using a larger set of buildings.
We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from 537 buildings. We also provide conclusions regarding the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging automated methods. Finally we discuss the potential evolution of M&V to better support the energy efficiency industry through low-cost approaches, and the long-term agenda for validation of building energy analytics.
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