You are here

Measurement & Verification with Green Button Data

April 13, 2012 - 4:15pm

Addthis

Measurement and verification (M&V) focuses on ensuring that the savings from energy efficiency projects are being realized with a certain degree of confidence. M&V involves understanding how energy savings arebeing realized from a project; designing a cost-effective assessment strategy that addresses how to ensure the savings can be measured; and implementing the designed strategy by gathering the key data followed by analysis and reporting of the actual savings. In some cases, data from the Green Button program can be used to assess the energy savings from new efficiency project.

M&V is generally conducted using IPMVP guidelines. These are broad principles that can be applied to a wide range of energy savings projects for different applications. The U.S. Department of Energy M&V Guidelines: Measurement and Verification for Federal Energy Projects, which are an application of IPMVP, are tailored specifically to federal energy savings performance contracts (ESPC). American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Guideline 14 on Measurement of Demand and Energy Savings, 2002 provides more specific details on M&V and is intended to provide a minimum acceptable level of metering-based measurement of energy and demand savings from energy management projects.

Broadly, M&V techniques can be applied either at the energy conservation measure (ECM) level, by isolating the retrofit and measuring the performance variables that are affected by the ECM (IPMVP Option A and B), or at the whole building level using the facility’s energy consumption data (IPMVP Option C - whole facility). Whole building energy consumption data can be used to assess the overall impact of all the implemented ECMs in a particular building. This aggregate approach does not evaluate the performance of each individual measure but instead provides an overall cumulative savings estimate from all the implemented measures in the building.  Since whole-building meter data are used for this analysis, it involves making adjustments to account for any non-ECM related changes. This can be due to variations in the weather, occupancy oroperating hours (referred to as independent variables by IPMVP). Mathematical modeling (like regression analysis) can help to identify the key independent variables that affect building energy consumption.  In order to develop this model, the data for independentvariables – e.g., the number of heating degree days (a measure of how cold the winter is) – have to be collected along with the associated energy consumption data. The higher the granularity of these data, the less uncertain the developed model will be (a model developed using 15-minute data tends to have less uncertainty than a model developed using daily data, for instance). This whole-building M&V strategy is intended for projects where the ratio of expected savings to baseline energy consumption is large enough to make the savings distinguishable from the random variations in energy data. More detailed discussion on Option C M&V analysis can be found in BPA’s Regression for M&V: Reference Guide.

The yearly energy consumption can be divided into weather dependent (heating and cooling) and non-weather dependent. Consequently, ECMs savings are either weather dependent or independent. The energy consumption when plotted as a function of outside air temperature can help identify these components by developing linear models (parameter change point models ASHRAEResearch Project 1050). For example, cooling unit energy consumption is typically directly proportional to the outside weather, in other words the hotter theoutside weather (above a threshold temperature, referred to as balance point) the more the cooling energy consumption. On the other hand the energy consumed by lighting, refrigerator, or washer are not dependent on the weather.

High frequency Green Button data can be used to employ the whole building M&V strategy (IPMVP Option C). This Green Button electricity data can be analyzed to assess the effectiveness of the various energy conservation measures implemented in the building by developing a baseline model (as a function of independent variables). The energy savings can be calculated by comparing the adjusted baseline energy consumption (consumption before implementing ECMs after adjusting for weather and/or other independent variables) with the post-retrofit energy consumption. A simple example can be an application to compare same month utility data from one year to the next but with adjustment for weather. The limitation of this approach is that the impact of each individual ECM will not be known but only the cumulative effect of all the ECMs. Also another limitation is identifying the possible independent variables and collecting that data in association with the corresponding energy consumption data to develop the baseline models.

Addthis