Measurement & Verification
What is Measurement & Verification (M&V)?
Measurement and Verification, or M&V, is a process of planning, measuring, collecting and analyzing data to verify and report energy savings within a facility or facilities resulting from the implementation of energy-efficiency measures.
Energy Modeling for M&V
This Energy Modeling Protocol provides guidance to verify energy savings for energy conservation measures (ECMs) implemented in commercial buildings, industrial facilities, or their subsystems. This protocol is appropriate to verify savings for ECMs that deliver large savings through high impact single ECMs or multiple smaller impact ECMs distributed throughout a building or facility. Verifying savings from individual ECMs applied to single end uses or equipment is not a good application of this protocol.
These methods are based on and extend the descriptions of the whole building method found in the International Performance Measurement and Verification Protocol (IPMVP) under Option C and in American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14-2014, as well as a large volume of applied research extending back to the early 1970s. This protocol extends the application of whole building energy modeling to smaller measurement boundaries around facility subsystems, such as chilled water systems, air handling systems, or industrial processes. Such applications are considered retrofit isolation methods under IPMVP Option B (All Parameter Measurement) or ASHRAE Guideline 14-2014.
This protocol describes procedures for collecting and preparing necessary baseline and postinstallation data, and for developing appropriate empirical (that is, statistical or regression-based) models for use in calculating a project’s energy savings. The methods described here are useful when the expected savings are large in comparison with the uncertainty of the empirical energy model. This protocol expands on the guidelines for performing regression analysis provided in BPA’s Regression for M&V: Reference Guide, with a focus on developing and validating energy models
The effect of selected independent variables on a building or subsystem’s energy use is modeled using statistical regression techniques. This enables the baseline energy use to be projected into or adjusted to conditions occurring in the post-installation period. Savings are then determined by subtraction of the adjusted baseline and measured post-installation energy usages. The savings may also be determined for conditions other than the post-installation period, such as to typical meteorological year (TMY) weather conditions. This requires a post-installation period energy model
Advantages
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Use of energy modeling has several advantages because it:
- Uses measured energy and independent variable data to account for savings
- Verifies the impact of all ECMs implemented within the selected measurement boundary
- Leverages large volumes of research on degree-day methods, change-point models, and non-linear and multiple regressions
- Is supported by public and commercially-available data preparation and analysis tools
- Estimates savings uncertainty
- Tracks savings over long period
Disadvantages
This protocol is usually not appropriate when sponsoring parties require the calculation of savings from individual ECMs amongst multiple ECMS within a measurement boundary. It cannot be applied when the monitoring systems are not in place and hence there is no available data. Its methods require a familiarity with statistical regressions, a skill not always available among service providers. This protocol implicitly assumes an existing condition baseline. A different protocol is needed for ECMs where a current practice baseline is appropriate because of program guidelines or because the existing equipment had reached mechanical failure and no longer represents a viable baseline
The useful tools that are available require time to become familiar with them. Furthermore, at present there is no single tool that provides all the capabilities needed, as discussed in Chapter 7 of this protocol. In most circumstances, users must leverage multiple tools to follow the guidance in this protocol.
While not necessarily a disadvantage of this protocol, it should be noted that the Energy Modeling Protocol may need to be supplemented by a different protocol to quantify energy usage impacts due to identified Non-Routine Events occurring within the measurement boundary.