EVO is pleased to announce the release of a new application guide Uncertainty Assessment for IPMVP (EVO 10100: 1:2018). This new document is a revised and expanded version of the Statistics and Uncertainty for IPMVP (EVO 10100 1:2014) application guide published in 2014. The new application guide includes examples for each of the four IPMVP options.

M&V efforts are designed to determine energy and demand savings with some reasonable accuracy. However, this objective can be affected by either systematic error (i.e., not occurring by chance, including measurement error) or random error (i.e., occurring by chance and often due to using a sample rather than a census to develop the measured value).

Uncertainty is an assessment of the probability that an estimate is within a specified range from the true value. It is an overall indicator of how well a calculated or measured value represents a true value. Without some assessment of uncertainty, it is impossible to judge an estimate’s value as a basis for decision-making.

Measurement errors, defined as the difference between estimates and true values, are an expected outcome of any analytical method. Errors can be introduced at every stage of the measurement and verification (M&V) process, including sampling, measurement, and adjustment. It is nearly impossible to quantify the effect of every potential source of error. M&V reports often limit uncertainty discussions to random error (especially sampling error and regression error). However, reasonable effort should be made to identify and attempt to minimize every potential source of uncertainty.

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EVO’s IPMVP Committee has undertaken the development and revision of several IPMVP application guides. This is the second of a series of guides to be published as complementary documents to the IPMVP Core Concepts 2016.

EVO would like to thank the IPMVP Uncertainty Assessment sub-committee for the sustained efforts deployed in the upgrade of this application guide.

Committee Chair: M. Sami Khawaja, Ph.D. – Cadmus
Brian Ball, Ph.D. – National Renewable Energy Laboratory
Dakers Gowans, P.E. – Left Fork Energy, Inc.
David Jump, Ph.D., CMVP – kW Engineering
Bill Koran, P.E., CMVP – SBW
Dave Korn, P.E. – Ridgeline Energy Analytics
Steve Kromer, P.E. – SKEE
Jesse Smith, M.Sc. – Demand Side Analytics
Kevin Warren, P.E. – Warren Energy Engineering