Introduction
Building commissioning is a systematic approach aimed at reducing energy consumption and enhancing indoor environmental quality for occupants Retro-commissioning (RCx) focuses on existing structures, emphasizing the identification of performance goals, deficiencies, and opportunities for improvement This process involves making necessary adjustments through low-cost repairs and more significant retrofits, alongside measurement and verification (M&V) techniques to ensure operational enhancements Advanced strategies include utilizing utility data to pinpoint suitable buildings, integrating building automation systems with fault detection tools to communicate opportunities to stakeholders, and executing automated control system adjustments.
This document outlines a conceptual framework for validating the benefits of RCx-related technology innovations compared to current practices, serving as a basis for creating site-specific measurement and verification (M&V) plans It reviews M&V practices that assess energy savings from these technologies and presents a methodology for evaluating post-installation performance against baseline data Additionally, it details project hypotheses, technical objectives, success indicators, and criteria for selecting optimal demonstration sites, which include facility size, characteristics, the number of locations for generalizable conclusions, and necessary performance data An Appendix provides a demonstration site questionnaire to facilitate site selection efforts.
Measurement and Verification: An Overview
Energy savings cannot be measured directly as they reflect the absence of energy consumption Instead, they are assessed by comparing energy usage before and after implementing energy conservation measures, with the initial energy use referred to as the baseline.
The "after" case, known as the post-installation or performance period, is crucial for accurately determining energy savings It is essential to adjust for factors that influence energy use but are not directly related to the implemented measures, such as variations in weather, occupancy, and other conditions that may differ between the baseline and performance periods Equation 1 outlines the general formula utilized for calculating these savings.
Savings = (Baseline Energy - Post Installation Energy) ± Adjustments Eqn 1
Equation 1 can be reformulated to eliminate the need for adjustments in post-installation energy measurements This involves creating a regression model based on baseline energy usage, utilizing independent variables to estimate the expected baseline energy usage under post-installation conditions Additionally, both baseline and post-installation energy usage can be recalibrated to reflect conditions other than the original baseline or post-installation scenarios.
M&V protocols to determine savings in energy conservation projects have existed since about
1995 Notable protocols include the International Performance Measurement and Verification Protocol (IPMVP) – Volume I, the Federal Energy Management Program (FEMP) M&V
Guidelines for Federal Energy Projects, and ASHRAE Guideline 14.
The IPMVP Volume I (2014) serves as a conceptual framework for measuring, computing, and reporting the savings from energy or water efficiency projects in buildings It outlines essential definitions and considerations for creating a Measurement and Verification (M&V) plan, though it does not delve into specifics regarding particular measures or technologies Created through collaboration among industry, government, financial institutions, and other organizations, the IPMVP document presents four M&V options and addresses relevant issues for third-party-financed and utility projects.
The FEMP M&V Guideline (2008) provides detailed procedures for implementing the concepts from the 2007 IPMVP Volume I, specifically tailored for federal projects It outlines methods for determining measurement and verification (M&V) approaches, assessing M&V plans and reports, and establishing payment criteria for energy savings throughout the contract These procedures ensure full compatibility and consistency with the IPMVP framework.
ASHRAE Guideline 14 (2002) serves as a crucial reference for calculating energy and demand savings linked to Measurement and Verification (M&V) activities It outlines instrumentation and data management protocols while addressing uncertainty in models and measurements The guideline details three engineering approaches to M&V, each requiring that the overall uncertainty of savings estimates remain below specified thresholds These approaches align with the options in IPMVP Volume I, although Guideline 14 does not cover aspects of performance contracting.
M&V activities encompass site surveys, energy metering, independent variable measurement, engineering calculations, and reporting The application of these activities to assess energy savings varies based on the specific characteristics of the energy conservation measures (ECMs) implemented, aiming to balance the precision of energy savings estimates with the associated costs of M&V.
IPMVP Volume I outlines four measurement and verification (M&V) protocol options, allowing for the application of diverse techniques tailored to various applications, as summarized in Section 3 of this document and detailed in excerpts from Chapter 4 of the FEMP Guideline.
Option A (Retrofit Isolation with Key Parameter Measurement).
Option B (Retrofit Isolation with All Parameter Measurement).
A graphical representation of the savings impact from Measurement and Verification (M&V) is illustrated in Figures 1 and 2, based on data collected during a monitoring-based commissioning project (MBCx) at UC Berkeley’s Soda Hall Utilizing the IPMVP Option B approach, the energy consumption of HVAC systems, including chillers, pumps, and air handlers, was monitored for three months before implementing operational efficiency improvements As depicted in Figure 1, a baseline energy model was established through a simple linear regression analysis of daily energy use in relation to ambient dry-bulb temperature.
Baseline Data Base Model Post Data Post Model
Figure 1 Scatter plot of daily HVAC energy use vs. ambient temperature (2-parameter model).
Figure 2 illustrates the statistical indices, specifically the root-mean square error (RMSE), highlighting the energy savings achieved during the post-installation period Throughout this time, data on ambient temperature and energy consumption were consistently gathered, allowing for a comparison between the projected baseline energy use and the actual measured consumption The savings are represented as the difference between the baseline model's projections and the observed energy use following the installation.
Figure 2 effectively illustrates the Measurement and Verification (M&V) concept, showcasing its ability to clearly demonstrate energy savings to project and program sponsors This visualization is easy to comprehend and offers valuable insights into the energy consumption of HVAC systems.
Demonstrates the dependence of HVAC energy use with ambient temperature.
Demonstrates magnitude of energy savings each day.
Creates new baselines for additional projects.
The post-installation model, represented by the orange line, serves as a benchmark for evaluating energy performance over time By comparing this model with actual energy usage in the post-installation period, it becomes possible to identify any decline in energy efficiency This approach not only highlights potential performance issues but also facilitates ongoing monitoring of energy consumption.
HVAC Daily kWh Usage Baseline Post-Install Model
Baseline Model: kWh = 79.9*OAT + 1129 RMSE = 136 kWh
Baseline Period Post-Installation Period
Post-Install Model: kWh = 44.1*OAT - 336 RMSE = 213 kWh
Figure 2 Representation of the M&V concept over time.
M&V Options: Summary of Approaches
Option B: Retrofit Isolation With All Parameter Measurement
M&V Option B involves either periodic or continuous measurement of all energy quantities and parameters necessary for calculating energy during the performance period While this method offers the highest accuracy in determining savings, it also leads to increased costs associated with measurement and verification during the performance period.
Option B is typically used when any or all of the following conditions apply:
For projects focused on replacing simple equipment that achieve energy savings of less than 20% of the total energy consumption of a facility, as indicated by the appropriate utility meter or sub-meter readings, Option C is not applicable.
When energy savings values per individual measure are desired.
When interactive effects can be estimated using methods that do not involve long-term measurements.
When the independent variables that affect energy use are not complex and excessively difficult or expensive to monitor.
When operational data on the equipment are available through control systems.
When sub-meters already exist that record the energy use of subsystems under consideration (e.g., a separate sub-meter for HVAC systems).
Option B procedures focus on physically assessing equipment changes to ensure installations meet specifications Savings potential is validated through observations, inspections, and various metering methods, such as using variable frequency drive speed as a proxy for motor power Baseline models are created by correlating metered energy use with key independent variables For certain energy conservation measures (ECMs), spot or short-term metering may suffice to establish baseline conditions, while continuous metering may be conducted post-retrofit Spot measurements are suitable for the performance period when performance variations are minimal, supporting normalized savings approaches with necessary adjustments to baseline models In cases like retrocommissioning where variations are anticipated, continuous measurement is essential This ongoing monitoring not only tracks energy use but also enhances equipment operation over time, optimizing retrofit performance.
Option C: Whole-Building Data Analysis
Option C estimates energy savings by creating statistically representative models of overall building or sub-metered energy usage, such as therms and kWh While this approach validates total energy savings, it does not quantify the savings attributed to specific components.
Option C is recommended for complex equipment replacement and control projects that yield significant energy savings, typically exceeding 10% to 20% of a building's monthly energy use For buildings with reliable data and specific uncertainty criteria, savings below 10% may still be assessed using Option C This approach is particularly effective for analyzing interactions between energy systems and evaluating the impact of indirect measures, like insulation However, implementing Option C requires skilled analysts and should only be used for projects that satisfy established criteria.
Savings are predicted to be greater than about 10% to 20% of the overall consumption measured by the utility or sub-meter.
At least 12 and preferably 24 months or more of pre-installation data are used to calculate a baseline model.
At least 9 and preferably 12 months of performance period data are used to calculate annual savings.
Adequate data on independent variables are available to generate an accurate baseline model, and procedures are in place to track the variables required for performance period models.
Significant operational or other changes are not planned for the facility during the performance period, and procedures are in place to document changes that do occur at the site.
Again, these guidelines may be relaxed if interval meter data are available, and depending on building predictability, and project uncertainty requirements.
Option C involves creating regression models to accurately predict energy consumption by considering relevant independent variables specific to the project While some may resort to basic mathematical methods like utility bill comparisons, these approaches are often unreliable and not advisable In contrast, regression models effectively incorporate the effects of weather and other influencing factors on energy usage, which simple utility bill comparisons fail to do.
The Option C approach involves creating a baseline model that connects baseline energy usage to key independent variables, while continuously monitoring energy use and these variables during the performance period Energy savings are typically determined by comparing the energy predicted by the baseline model under measured conditions with the actual energy consumption during the performance period Alternatively, performance period models can be developed to adjust both baseline and performance data to typical conditions before comparison, especially when complete yearly data for the performance period is unavailable.
Effective utility data analysis hinges on the collection, validation, and appropriate application of data Option C techniques leverage three key data types: utility billing data, independent variables, and information regarding unrelated site changes.
Utility billing data is essential for calculating savings, as it enables the comparison of adjusted baseline models with energy usage during the performance period To effectively utilize this data, it is crucial to align the start and end dates of the utility data with those of the independent variables By collecting data on independent variables more frequently than billing data, you can better synchronize the time frames for accurate analysis.
Monthly billing data is essential for understanding usage patterns, and it should be measured at least once a month There are two main types of monthly billing data: total monthly usage and usage segmented by time-of-use periods While both types can be utilized in regression models, time-of-use data is more advantageous as it offers deeper insights into consumption behaviors Additionally, peak demand is often recorded, further enhancing the analysis of usage trends.
Interval demand billing data captures the average energy consumption over specified intervals, such as every 15 minutes, throughout the billing cycle This data often includes charges for peak demand, reflecting the highest energy use during the billing period.
Stored energy billing data Inventory readings or delivery information can be used to determine historical consumption for resources such as fuel oil, although sub-metering is preferred.
One challenge in implementing Option C is addressing factors outside of the Energy Conservation Measure (ECM) that influence overall site energy consumption, such as variations in square footage or load demands, known as 'non-routine adjustments.' Monitoring site changes is essential for accurately reflecting energy use changes unrelated to ECM installations However, effectively tracking the necessary data for these non-routine baseline adjustments can be difficult, particularly for long-term contracts and sites experiencing substantial operational shifts.
Option D: Calibrated Simulation
Option D utilizes calibrated computer simulations conducted by experienced analysts to model building and mechanical systems, predicting energy use before and after the implementation of Energy Conservation Measures (ECMs) The models' accuracy is enhanced by incorporating metered site data to align with baseline and performance conditions Well-constructed models can estimate savings for various ECMs within a project, with more complex models typically offering improved accuracy in savings calculations, albeit at a higher cost.
Option D methods should be used only for projects that meet any or all of the following requirements:
For complex equipment replacement and controls projects with too many ECMs to cost- effectively use retrofit isolation methods.
When interactive effects between ECMs are too complex for retrofit isolation approaches, but need to be quantified.
When the Option C approach is not viable due to expected savings on the order of less than 10-20% of metered use.
When complex baseline adjustments are expected during the performance period.
When energy savings values per individual measure are desired.
When new construction projects are involved.
When savings levels are sufficient to warrant the cost of simulation.
When either baseline or performance period energy data, but not both, are unavailable or unreliable.
Option D is particularly beneficial in scenarios lacking a baseline, such as new construction or significant building modifications It is also advantageous when the factors contributing to savings, like decreased solar gain and heat loss through upgraded windows, are challenging to quantify.
Situations for which computer simulation is not appropriate include:
Analysis of ECM savings that can be more cost-effectively determined with other methods.
Buildings that cannot be adequately modeled, such as those with complex geometries or other unusual features.
Building systems or ECMs that cannot be adequately modeled, such as radiant barriers or demand-response control algorithms that are important in comparing baseline and performance period scenarios.
Projects with limited resources that are not sufficient to support the effort required for data collection, simulation, calibration, and documentation.
M&V Option D for existing buildings involves five key steps: 1) data collection, 2) data input and baseline model testing, 3) baseline model calibration, 4) performance period model creation and refinement, and 5) performance verification and savings calculation For comprehensive technical guidance on these steps, refer to the Federal Energy Management Program’s M&V Guidelines: Measurement and Verification for Federal Energy Projects Additionally, ASHRAE Guideline 14 outlines a more detailed eight-step process.
Input data and run the baseline model
Create and refine the performance period model
Verify performance and calculate savings
The methodology for new construction projects, as outlined in IPMVP Volume III, differs significantly from that of existing buildings, primarily due to the availability of utility data While the performance period model for new constructions can be calibrated using utility data, the baseline model cannot, as it lacks sufficient data However, comparisons with similar buildings can still be utilized This same approach is applicable to existing buildings that do not have reliable baseline energy data.
Whole-building energy simulation programs, such as eQUEST and EnergyPlus, are widely utilized for energy analyses These tools create customized models of buildings and their systems, using hourly weather data to accurately predict energy consumption.
Building simulation programs necessitate comprehensive input data to effectively model a building's energy consumption Recent advancements in user interfaces have streamlined the input process through graphical formats, while the inclusion of libraries featuring standard building components has enhanced the efficiency of model development.
Simulation programs acceptable for Option D should have the following characteristics:
The program is commercially available, supported, and documented.
The program has the ability to adequately model the project site and ECMs.
The model can be calibrated to an acceptable level of accuracy.
The program allows the use of actual weather data in hourly format.
Model calibration for existing buildings involves aligning simulation inputs with actual operating conditions and comparing the results against whole-building or end-use data This process can focus on either the entire building or specific systems affected by energy conservation measures (ECMs) Both baseline and performance models should undergo calibration whenever feasible, typically through an iterative process of adjusting inputs and reassessing results against measured data A model is deemed calibrated once it meets the specified statistical indices Calibration requirements should be clearly outlined in the project-specific Measurement and Verification (M&V) Plan and adjusted as necessary to accommodate project needs.
For most models, there are multiple levels of calibration that can be performed:
System level calibration with hourly monitored data.
Whole-building level calibration with monthly utility data.
Whole-building level calibration with hourly utility data.
The required level of calibration for a project is influenced by its value, data availability, and the necessity for precise savings estimates At a minimum, all models should be calibrated using monthly data, while simulation models targeting specific systems should utilize system-level data For enhanced accuracy, particularly in assessing peak demand savings, calibrating models to hourly data is essential Additionally, when calibrating a computer simulation with measured utility data, it is crucial to incorporate actual weather data.
Developing Regression Models for M&V
Independent Variables
Independent variables can be sourced from third-party providers or gathered through onsite data collection, depending on their characteristics Weather data, in particular, tends to be more dependable when obtained from an external source; however, it is essential to validate this information with onsite data to confirm its relevance and accuracy.
After collecting the data, a mathematical model is created to predict the baseline energy usage during the performance period This model must be logically sound, with independent variables that are reasonable, and coefficients that align with expectations, displaying the correct signs (positive or negative) and remaining within a plausible range of values.
Choosing a Model
There are various forms of models used in standard statistical practice Examples of multi-variant regression models are included in the IPMVP document and ASHRAE Guideline 14.
A linear multi-variant regression model for a weather-dependent Energy Conservation Measure (ECM) is illustrated in Equation 2 When utilizing weather data, establishing a customized temperature baseline for the calculation of Heating Degree Days (HDD) and Cooling Degree Days (CDD) can enhance the model's accuracy by reflecting the building's actual performance.
E = B1 + (B2×Ti−Ti−1)+(B3×HDDi)+(B4×CDDi)+(B5×X1)+(B6×X2)+(B7×X3) Eqn 2 where:
E = energy use i = index for units of time for period
HDD = heating degree days using a base temperature of 60ºF
CDD = cooling degree days using a base temperature of 65ºF
Xn = independent steady-state variables
Choosing the optimal model type is crucial and depends on the number of independent variables influencing energy use and the complexity of their relationships To identify the best model, it is often necessary to test multiple options and compare their statistical outcomes The number of coefficients must align with the number of observations, and the polynomial form should correspond appropriately to the number of independent variables Furthermore, it is essential that the independent variables are genuinely independent from each other Lastly, the model should be assessed for potential statistical issues, such as auto-collinearity, and necessary corrections should be made.
Validation steps must ensure that statistical results adhere to acceptable standards, with examples provided in Table 2 of the FEMP M&V Guidelines ASHRAE Guideline 14 outlines validation criteria for statistical models based on data resolution, such as monthly or hourly measurements It is essential to establish specific validation goals for mathematical models in each project, tailored to the level of effort required, and documented in the site-specific M&V Plan While some analysis tools automatically generate these statistical results, others may require separate calculations.
A coefficient of determination (R²) greater than 0.75 signifies that the model effectively explains the variability in the dependent variable Conversely, lower R² values suggest that there may be missing independent variables or a need for additional data to enhance the model's accuracy.
Coefficient of variation of root-mean squared error
CV(RSME) < 15% Calculates the standard deviation of the errors, indicating overall uncertainty in the model.
The Mean Bias Error (MBE) serves as a key indicator of bias in regression estimates, with a threshold of +/- 7% Positive MBE values suggest that the regression model tends to over-predict actual outcomes, while negative values indicate an under-prediction Additionally, a t-statistic greater than 2.0 signifies that the independent variable is statistically significant, highlighting its importance in the regression analysis.
Selecting an M&V Approach: IPMVP Options A-D
M&V Considerations for Option A
Option A is designed for projects that require verification of potential savings, allowing for actual savings to be assessed through short-term measurements, estimates, and engineering calculations Key considerations when implementing Option A include the accuracy of measurements and the reliability of estimates in determining financial benefits.
Option A methods exhibit varying degrees of accuracy in assessing savings and verifying performance, with the precision influenced by the reliability of estimates, the quality of the equipment inventory, and the measurements taken.
Verifying proper ongoing operation and potential to perform is an important aspect of Option A.
Option A is suitable for straightforward Energy Conservation Measures (ECMs) where the baseline and post-installation conditions, including factors like equipment quantities and ratings such as lamp wattages or motor kilowatts, contribute significantly to the project's overall uncertainty.
Option A methods are not suitable for ECMs whose performance is uncertain or unpredictable.
In general, comprehensive retro-commissioning projects will not lend themselves well to Option
M&V Considerations for Option B
Some considerations when using Option B approaches include:
All end-use technologies can be verified with Option B; however, the degree of difficulty and costs associated with verification increases as metering complexity increases.
Measuring energy savings through Option B can be more challenging and expensive compared to Option A; however, the results obtained from Option B tend to be more accurate.
Periodic spot or short-term measurements of factors are appropriate when variations in loads and operation are not expected.
Continuous measurements are essential for capturing operational variations, leading to more accurate estimates of actual energy savings Additionally, they offer valuable long-term data on the energy consumption of equipment or systems, ensuring sustained efficiency.
Data gathered for energy savings assessments can enhance real-time equipment operation, maximizing the advantages of retrofitting However, for constant-load retrofits, the advantages of ongoing measurements may not exceed those of short-term evaluations.
M&V Considerations for Option C
The following points should be considered when conducting Option C utility data analyses for M&V:
To accurately assess energy consumption, it is essential to identify all independent variables that influence it, regardless of their inclusion in the model Key factors may encompass weather conditions, building occupancy levels, temperature set points, and the time of day Among these, outdoor air temperature stands out as the most significant variable for various energy consumption models (ECMs).
The specifications for any separate performance period models utilized must be clearly defined, including the relevant statistical validation targets It is essential to demonstrate the statistical validity of the final regression models to ensure their reliability and effectiveness.
Independent variable data need to correspond to the time periods of the billing meter reading dates and intervals A plan for data collection, including sources and frequencies, should be specified.
It is best to develop models using data in whole-year sets (12, 24, 36, or 48 months) so that any seasonal variations are not overstated.
To effectively monitor the impact of site changes not related to the installation of ECMs during the performance period, it is essential to outline the tracking methods for these changes Additionally, it is important to clarify how the collected data will be utilized for making necessary savings adjustments.
To ensure compliance with minimum energy and operating standards, such as ventilation rates and lighting levels, it is essential to accurately adjust baseline energy use Any modifications made to the energy model must be thoroughly documented to reflect these changes.
M&V Considerations for Option D
Many issues must be considered and addressed in developing a site-specific M&V Plan using Option D Some of the more common issues are outlined below:
To ensure accurate building models, it is essential to engage an experienced building modeling professional While modern simulation software simplifies many aspects of the process, inexperienced users may struggle to grasp the full capabilities of the program and the necessary real data requirements, potentially leading to inaccurate models.
Determine the availability of utility bill data.
To enhance the accuracy of energy modeling, it's essential to determine the availability of hourly or monthly billing data and the feasibility of installing meters for hourly data collection Hourly data typically offers more precise calibrations due to the increased number of comparison points, but it is often limited to a utility's largest customers or requires the use of Energy Management Control Systems (EMCS) or portable data loggers If only monthly billing data is accessible, implementing short-term monitoring of building sub-systems can significantly improve model accuracy.
Incorporate real equipment performance data into your simulation models to enhance accuracy Many software packages offer libraries of HVAC equipment that closely align with actual system performance; however, it’s essential to thoroughly review the HVAC descriptions in these libraries to ensure they accurately reflect the real systems Additionally, consider creating user-defined equipment performance curves based on field measurements or manufacturer data for improved reliability.
For effective building performance assessment, it is essential to conduct spot measurements and short-term monitoring of key parameters during both the baseline and performance periods These measurements enhance the overall data quality and provide a more accurate representation of building systems Monitoring should encompass a full operational range, such as spring and summer for cooling systems, and data collection must allow for sub-system level calibration However, careful selection of these measurements is crucial, as they can significantly increase project costs and timelines.
Utilizing trend data is essential for accurately assessing control performance in building systems Interpreting the sequencing of building controls can be challenging when relying solely on interviews, site surveys, manufacturer data, and isolated measurements Therefore, the most effective method to understand actual control sequences is by analyzing trend data.
Sometimes, the EMCS systems can be utilized to determine actual operating scenarios However, the capability for data collection and storage in many systems may be limited.
This article outlines the necessary model calibration procedures for monthly, hourly, and subsystem data applicable to both baseline and performance period models It emphasizes the importance of establishing statistical calibration requirements tailored to meet the project's accuracy standards.
Specify the simulation program and version and the source of weather data used (onsite, local weather station, or typical weather data).
Clearly explain how savings will be calculated after the first year Keeping models up to date can be expensive For projects without substantial site changes expected, an Option
C utility billing analysis approach may be viable Regardless of how savings are calculated each year, the ongoing performance of the measures needs to be verified periodically.
Developing an M&V Plan
Regardless of the M&V option chosen, for M&V to be useful, one needs to confirm that:
The baseline conditions are accurately defined.
The proper equipment/systems are installed and properly commissioned.
The equipment/systems are performing to specification.
Although confirming these three items may appear simple, a structured approach is helpful The following describes six steps for this purpose:
Site-specific M&V Plans require that responsibilities for key activities be assigned to the appropriate stakeholders involved For example, for an Energy Savings Performance
When allocating risks and responsibilities in a Contractual Energy Service Performance Contract (ESPC), it is essential to consider various financial, operational, and performance issues The distribution of these responsibilities will vary based on each stakeholder's available resources, preferences, and their ability to provide and act on relevant information regarding these factors.
A few fundamental principles can be applied to the allocation of responsibilities:
Logic and cost-effectiveness drive the allocation of responsibilities.
The responsible party predicts its likely tasks and associated costs to fulfill its responsibilities, and makes sure these are covered in the project budget.
Stipulating certain parameters in the M&V Plan can align responsibilities, especially for the items no one controls.
Step 2: Develop a Site-Specific M&V Plan
A site-specific M&V Plan may be the single most important item in determining energy savings The plan defines how savings will be calculated and specifies activities that will occur during M&V.
Before initiating an Investment Grade Audit (IGA), it is crucial for stakeholders to reach a consensus on the Measurement and Verification (M&V) approaches to be employed Although the M&V Plan is typically formulated during contract negotiations, the selected M&V methods significantly influence the audit's activities, as well as its overall cost and duration.
The project-specific M&V Plan includes project-wide items as well as details for each energy-conservation measure (ECM).
Overview of proposed energy and cost savings.
Utility rates and the method used to calculate cost savings.
Details of baseline conditions and data collected.
Documentation of all assumptions and data sources.
Details of any engineering analyses performed.
The method by which energy savings will be calculated.
Details of any O&M or other cost savings claimed.
Details of proposed energy and cost savings.
Details of post-installation verification activities, including inspections, measurements, and analysis.
Details of any anticipated routine adjustments to baseline or reporting period energy.
Content and format of all M&V reports (post-installation and periodic M&V).
In the Measurement and Verification (M&V) process, the baseline is defined by the entity conducting the Initial Gap Assessment (IGA) and includes key physical conditions such as equipment inventory, occupancy schedules, and energy consumption rates This baseline is established through various methods like surveys, inspections, and short-term metering, with utility bills often serving as verification The primary purpose of defining the baseline is to estimate energy savings by comparing it with post-installation energy use Additionally, baseline information is crucial for adjusting energy use to account for changes during the performance period When employing whole building metering or calibrated simulation, it is essential to document the baseline energy use for all end uses, not just those impacted by the retrofit.
After implementing an Energy Conservation Measure (ECM), it may not be possible to reevaluate the baseline conditions unless the ECM can be disabled to revert the building to its pre-retrofit state Therefore, it is crucial to accurately define and document the baseline conditions The decision on what to monitor and the duration of monitoring should consider factors such as the complexity of the ECM, the stability of the baseline, and the variability of equipment loads and operating hours, along with other variables influencing the load.
Step 4: Install and Commission Equipment and Systems
Commissioning installed equipment and systems is a best practice in the industry, ensuring that all systems are designed, installed, and functionally tested to meet design intent, including optimal lighting levels, cooling capacity, and comfortable temperatures Typically carried out by the technology implementer and observed by building representatives, commissioning can also be outsourced to third-party professionals when necessary.
Commissioning activities, which encompass inspections and functional testing, are detailed in a Commissioning Plan, with results documented in a Commissioning Report For in-depth guidance on commissioning related to technology implementation projects, refer to Section 8 of the FEMP Guideline.
Commissioning involves performance measurements to verify that systems operate correctly, while post-installation Measurement and Verification (M&V) focuses on assessing the energy efficiency of these systems Although commissioning and post-installation M&V activities may overlap, their primary distinction lies in the fact that commissioning ensures proper system functionality, whereas post-installation M&V quantifies energy performance.
Step 5: Conduct Post-Installation Verification Activities
Post-installation M&V activities include surveys, inspections, spot measurements, and short- term metering The Post-Installation Report includes:
Detailed list of installed equipment.
Details of any changes between the proposed and as-built conditions, including any changes to the estimated energy savings.
Documentation of all post-installation verification activities and performance measurements conducted.
Performance verification - how performance criteria were met.
Documentation of construction-period savings (if any).
Status of rebates or incentives (if any).
Expected savings for the first year.
In projects utilizing specific Measurement and Verification (M&V) methods, particularly Option A, post-installation verification is crucial as it represents the sole opportunity to measure and confirm energy savings For certain measures, where equipment performance and energy savings are anticipated to remain stable over time, post-installation data becomes the primary basis for savings calculations Following this, inspections are carried out to ensure that the equipment's performance capabilities are intact.
Step 6: Perform Regular-Interval Verification Activities
Annual audits are essential for ongoing measurement and verification (M&V) activities, ensuring the persistence of energy savings in projects This process involves confirming that the installed equipment and systems are well-maintained, functioning correctly, and capable of delivering the anticipated savings.
An Annual Report serves to document annual Measurement and Verification (M&V) activities and report yearly savings However, implementing more frequent verification activities is often beneficial Regular monitoring and inspections ensure the effectiveness of M&V systems, confirm that installed equipment operates as intended, and enable adjustments based on ongoing operational feedback, ultimately preventing unexpected outcomes at year-end.
The Annual Reports should include:
Results/documentation of performance measurements and inspections.
Verified savings for the year (e.g., energy, energy costs, O&M costs).
Comparison of verified savings with the guaranteed amounts.
Details of all analysis and savings calculations, including commodity rates used and any baseline adjustments performed.
Summary of operations and maintenance activities conducted.
Details of any performance or O&M issues that require attention.
Considerations for Technology Demonstration Projects
Technology demonstration projects such as those conducted through the General Service
Administration’s Green Proving Ground, and the Department of Energy’s High Impact
Technology (HIT) Catalyst program are designed to accelerate the adoption of high-potential yet under-adopted technologies for improved building energy efficiency
The project hypotheses, technical objectives, success indicators, and tailored measurement and verification (M&V) plans are collaboratively created by the sponsoring agency, technology vendor, and subject matter experts In evaluating the technology, it is essential to consider not only energy and demand savings but also utility cost reductions, enhanced occupant comfort, and the benefits to operational and energy management staff.
In evaluating demonstration programs, it is crucial to consider factors such as installation, operations, maintenance, and technology warranties, as these elements significantly influence market uptake and the overall applicability of the technology.
Given the desired outcomes of these programs, site selection is a critical component of each technology demonstration Key site selection considerations are summarized below:
Engagement and willing partnership of operational staff
Ownership and management models, and the participation of property management companies
The compatibility of systems and controls with advanced RCx technology is crucial, particularly in distinguishing between built-up systems and packaged systems Additionally, understanding the requirements linked to the building automation system is essential for effective implementation.
Degree to which the electrical infrastructure, and existing metering and monitoring systems can be leveraged to support the desired M&V approach.
Diversity of building types and climates across the demonstration cohort.
Quality and completeness of building documentation, e.g., electrical one-line diagrams and panel schedules, control sequences, and mechanical and electrical equipment.
Appendix A contains a general questionnaire that can be customized for technology-specific site selection Appendix B contains an overview of data analysis tools that can be used to streamline the M&V process.
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FEMP 2008 “M&V Guidelines: Measurement and Verification for Federal Energy Projects, Version 3.0” Prepared for Us Department of Energy, Federal Energy Management Program by Nexant, Inc., April.
Haberl, J.S, D.E Claridge, J.K Kissock, and T.A Reddy 1993 “Emodel: A New Tool for Analyzing Building Energy Use Data” Energy Systems Labs Report ESL-IE-93-03-33, Texas A&M University.
Kissock, J.K., Energy Explorer, software and user manual, available at: Available from http://academic.udayton.edu/kissock/http/RESEARCH/EnergySoftware.htm; accessed on May
In their 2002 final report for ASHRAE Research Project 1050, Kissock, Haberl, and Claridge developed a comprehensive toolkit for calculating various inverse building energy analysis models, including linear, change-point linear, and multiple-linear models This toolkit aims to enhance the accuracy and efficiency of energy analysis in buildings, contributing valuable insights to the field of energy engineering The report is published by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., based in Atlanta, GA.
In their 1994 study published in the ASHRAE Journal, Kreider and Haberl evaluated various methods for predicting hourly building energy usage The research, stemming from the 1993 Great Energy Predictor Shootout, identified the most accurate techniques for forecasting energy consumption in buildings Their findings, detailed in Volume 35, Issue 3, pages 72-81, provide valuable insights for improving energy efficiency in building management.
PECI 2004 “National Strategy for Building Commissioning” Portland Energy Conservation, Inc Report to U.S Department of Energy.
Piette, M., S Kinney, and P Haves 2001 “Analysis of an Information Monitoring and
Diagnostic System to Improve Building Operations” Energy and Buildings, Vol 33, pp 783- 791.
Subbarao, K., L Yafeng, and T.A Reddy 2011 “The Nearest Neighbor Method to Improve Uncertainty Estimates in Statistical Building Energy Models” ASHRAE Transactions, pp 459-471.
Wright, J 2011 “Chapter 11: HVAC Systems Performance Prediction” In Building
Performance Simulation for Design and Operation, J.L.M Hensen and R Lamberts, ed New
AHRI Air-Conditioning, Heating, and Refrigeration Institute
ASHRAE American Society of Heating, Refrigerating, and Air-Conditioning Engineers BAS Building Automation System
EMCS Energy Management Control System
HVAC Heating, Ventilating, and Air-Conditioning
IPMVP International Performance Measurement and Verification Protocol
LBNL Lawrence Berkeley National Laboratory
QuEST Quantum Energy Services and Technology
RMSE Root Mean Squared Error
U.S DOE United States Department of Energy
1 California commercial end-use survey (CEUS), available at www.energy.ca.gov/ceus, final report, p 8.
2 Matthew, P., S Kromer, O Sezgen, S Meyers, “Actuarial Pricing of Energy Efficiency
Projects: Lessons Foul and Fair,” Energy Policy, 2004
3 Available from http://energyalmanac.ca.gov/electricity/index.html; accessed on May 15, 2015.
4 Available from http://gov.ca.gov/press-release/7689/; accessed on May 15, 2015.
5 S Kromer, “Efficiency Valuation: How to ‘Plan, Play, and Settle’ Energy Efficiency Projects,” Strategic Planning for Energy and the Environment, vol 27, no 1, 2007
6 Available from www.evo-world.org; accessed on May 15, 2015.
In their study presented at the National Conference on Building Commissioning in May 2007, Jump, Denny, and Abesamis explored the advantages of retro-commissioning through measurement and verification (M&V) results from two buildings Their findings, which can be accessed at www.peci.org/ncbc, highlight the effectiveness of retro-commissioning in enhancing building performance and energy efficiency.
The energy models mentioned are empirical models created using statistical techniques, based on data collected from building energy use and independent variables These models, which will be integrated into the tool, are detailed in ASHRAE Research Project 1050, accessible at www.ashrae.org as of May 15, 2015.
9 “Verification of Savings Project: Definition of Objectives Report,” California Commissioning Collaborative, available from: http://resources.cacx.org/library/ ; accessed on May 15, 2015.
10 Available from http://www.uccsuiouee.org/index.html; accessed on May 15, 2015.
11 “Verification of Savings Project: Existing Methods Report,” California Commissioning Collaborative, available from: http://resources.cacx.org/library/, search on “Cx Guidelines and Reports,” with Subheading “Measurement and Verification.” ; accessed on May 15, 2015.
12 “California Energy Efficiency Evaluation Protocols: Technical, Methodological, and
Reporting Requirements for Evaluation Professionals,” available from: www.calmac.org (search database on ‘Evaluation Protocols’).
13 Energy Explorer is a commercial software developed by Professor Kelly Kissock at the University of Dayton As the principal investigator of the ASHRAE Research Project 1050, which focuses on inverse energy models, Professor Kissock's work is accessible through the university's research page For more information, visit http://www.engr.udayton.edu/faculty/jkissock/http/RESEARCH/EnergyExplorer.htm (accessed May 15, 2015).
14 Please see results for project 37.01 Soda Hall in the UC/CSU/IOU Partnership MBCx Impact Evaluation, p 97 (the 37.01 project is actually a combination of two projects – Soda Hall and Tan Hall).
15 Please see evaluation results for the 04-05 Building Tune-Up Program, and the 04-05
UC/CSU/IOU Partnership MBCx Program, both available from www.calmac.org (search on the
“Impact Evaluation” category and “SBW Consulting, Inc.” as author) ; accessed on May 15, 2015.
16 Available from http://www.cacx.org/resources/rcxtools/spreadsheet_tools.html; accessed on May 15, 2015.
17 “2007 California Retrocommissioning Market Characterization”, California Commissioning Collaborative, April 2008
18 Emodel is proprietary software available only to subscribers of Texas A&M’s service- marked ‘Continuous Commissioning’ process, available from http://txspace.tamu.edu/handle/1969.1/2475; accessed on May 15, 2015.
The SuperESPC Measurement & Verification Annual Reports provide a comprehensive review for the US Department of Energy’s Federal Energy Management Program This report, accessible at http://ateam.lbl.gov/mv/docs/MV-Annual-Review-June-2002.doc#_Toc13035452, was last accessed on May 15, 2015, and offers valuable insights into energy management practices and verification methodologies.
In their paper presented at the National Conference on Building Commissioning in Newport Beach, CA, from April 22-24, 2008, Jump, Denny, Gallishaw, and Wiedwald explore the long-term financial implications of RCx (Retro-Commissioning) initiatives They question whether the benefits of such programs lead to lasting savings or if they ultimately remain unfulfilled For more details, the full paper can be accessed at www.peci.org/ncbc, with the last access noted on May 15, 2015.
22 Jump, D., K Kinney, M Denny, R Abesamis “Tracking the Benefits of Retro-
Commissioning – An Integrated Measurement and Verification Approach”, proc National Conference on Building Commissioning, San Francisco, April 19-21, 2006, available from www.peci.org/ncbc; accessed on May 15, 2015.
23 Jump, D, D Johnson, and L Farinaccio, “A Tool to Help Develop Cost-Effective M & V Plans”, proc American Council for an Energy Efficient Economy (ACEEE), Summer Study on Energy Efficiency in Buildings, Asilomar, CA, 2000
Rubenstein, Schiller, and Jump's 1998 study presented at the American Council for an Energy Efficient Economy (ACEEE) explores the concept of Standard Performance Contracting as a dual-purpose tool It emphasizes its effectiveness in enhancing energy efficiency while simultaneously facilitating market transformation in the building sector.
25 Energy Charting and Metrics Tool, presentation at the National Conference on Building Commissioning, Newport Beach, CA, April 2008.
26 Easy to Use Tools for Performance Monitoring Data Analysis, presentation at the ASHRAE winter meeting, New York City, January 2008.
27 Techniques and Tips for Retrocommissioning Energy Calculations, presentation at the National Conference on Building Commissioning, Chicago, May 2007.
28 Application of Clustering Techniques to Energy Data to Enhance Analysts’ Productivity, Proceedings of the 2002 Summer Study on Energy Efficiency in Buildings, American Council for an Energy Efficient Economy (ACEEE)
29 Internet-Based Building Performance Analysis Provided As a Low-Cost Commercial Service,
2001 International Conference for Enhanced Building Operations
30 DOE 2.1C Model Calibration with Short Term Tests versus Calibration with Long Term Monitored Data, Proceedings of the 1992 Summer Study on Energy Efficiency in Buildings, ACEEE
31 “M&V Value Tool Alpha Version Specifications” D Jump, available from http://ateam.lbl.gov/mv/index.htm; accessed on May 15, 2015.
32 Best Practices in HVAC Retrofit and Monitoring Based Commissioning (MBCx) – Shields Library, 2008, available from http://sustainability.calpoly.edu/tracks.html; accessed on May 15, 2015.
33 Best Practices in HVAC Retrofit and Monitoring Based Commissioning (MBCx) – Tan Hall,
2007, available from http://sustainability.ucsb.edu/conference2007/awards.php; accessed on May
34 SBW Report No 0606 - CALMAC Study ID QST0001.01, available from www.calmac.org; accessed on May 15, 2015.
35 1999 Nonresidential Large SPC Program Evaluation Study, available from www.calmac.org; accessed on May 15, 2015.
36 Nonresidential SPC M&V Case Study Report, April 30, 2002, available from www.calmac.org; accessed on May 15, 2015.
37 Mills, E., H Friedman, T Powell, et.al “THE COST-EFFECTIVENESS OF
The 2004 report titled "Commercial-Buildings Commissioning: A Meta-Analysis of Energy and Non-Energy Impacts in Existing Buildings and New Construction in the United States" provides a comprehensive evaluation of the benefits associated with commissioning in commercial buildings This analysis highlights both energy savings and other non-energy impacts, emphasizing the importance of effective building commissioning practices The full report is accessible at http://eetd.lbl.gov/emills/PUBS/PDF/Cx-Costs-Benefits.pdf, and it serves as a valuable resource for understanding the financial and operational advantages of commissioning in the commercial real estate sector.
ASHRAE Guideline 14-2002 Measurement of Energy and Demand Savings, ASHRAE ASHRAE
Guideline Project Committee 14P, George Reeves, Chair, 2002.
International Performance Measurement & Verification Protocol—Concepts and Options for Determining Energy and Water Savings, Efficiency Valuation Organization, March 2002.
M&V Guidelines: Measurement and Verification for Federal Energy Projects Version 3.0,
Prepared For U.S Department of Energy Federal Energy Management Program, Lia Webster and James Bradford, Nexant Inc April 2008.
A Best Practice Guide to Measurement and Verification of Energy Savings: A companion document to ‘A Best Practice Guide to Energy Performance Contracts’, The Australasian
Energy Performance Contracting Association for the Innovation Access Program of AusIndustry in the Australian Department of Industry Tourism and Resources, Australia 2004.
Measuring Energy-Savings Retrofits: Experiences from the Texas LoanSTAR Program, Haberl,
J., A Reddy, D Claridge, W Turner, D O’Neal, and W Heffington ORNL/Sub/93-SP090, 1996.
Review of Methods For Measuring And Verifying Savings From Energy Conservation Retrofits
To Existing Buildings, Jeff s Haberl, Ph.D., P.E and Charles H Culp, Ph.D., P.E Texas A&M
University Energy Systems Laboratory, September 2003, Revised April 2005.
Improving The Cost Effectiveness Of Building Diagnostics, Measurement And Commissioning Using New Techniques For Measurement, Verification And Analysis Steve Blanc et al., for
Development of a Tookit for Calculating Linear, Change-point Linear and Multiple-Linear Inverse Building Energy Analysis Models, ASHRAE Research Project 1050-RP Final Report
Kelly Kissock et al., ASHRAE November 2002.
Development of a Tookit for Calculating Linear, Change-point Linear and Multiple-Linear Inverse Building Energy Analysis Models, ASHRAE Research Project 1050-RP Detailed Test
Results Atch Sreshthaputra et al., ASHRAE May 2001, Updated August 2001
Literature Review Of Uncertainty of Analysis Methods (Inverse model toolkit), Jeff S Haberl,
Ph.D., P.E and Soolyeon Cho, Texas A&M Energy Systems Laboratory, October 2004
Inverse Modeling Toolkit: Numerical Algorithms, John Kelly Kissock et al., ASHRAE
Inverse Model Toolkit: Application and Testing, Jeff S Haberl et al., ASHRAE Transactions,
Effect of Time Resolution on Statistical Modeling of Cooling Energy Use in Large Commercial Buildings, ASHRAE Transactions 1995, Vol 101, Part 2, Katipamula, S., Reddy, T.A and
A Development and Comparison of NAC Estimates for Linear and Change-Point Energy Models for Commercial Buildings, Energy and Buildings, Vol.
A Baseline Model for Utility Bill Analysis Using Both Weather and Non-Weather Related
Variables, Robert C Sonderegger, Ph.D., ASHRAE Summer Meeting, Toronto, Canada, 1998
An Improved Cooling Tower Algorithm for the CoolTools TM Simulation Model, Dudley J Benton et al., ASHRAE Transactions 2002.
Development and Testing of the Characteristic Curve Fan Model, Jeff Stein and Mark Hydeman,
Development and Testing of a Reformulated Regression-Based Electric Chiller Model, Mark
Hydeman et al., ASHRAE Transactions 2002.
Development and Application of Regression Models to Predict Cooling Energy Consumption in Large Commercial Buildings, in the Proceedings of the 1994 ASME-JSME-JSES International
Solar Energy Conference, Katipamula, S., T Reddy, and D Claridge 1994 San Francisco, CA, pp 307-322.
Use of Simplified Models to Measure Retrofit Energy Savings, Katipamula, S., and D Claridge
ASME Journal of Solar Energy Engineering 115: 57-68, 1993
Emodel: A New Tool For Analyzing Building Energy Use Data, IETC Conference, Kelly
Monthly Variable-based Degree Day Template: A spreadsheet procedure for calculating a 3 parameter change-point model for Residential or Small Commercial Buildings, David S
Landman and Jeff S Haberl, Texas A&M University Energy Systems Laboratory, August 1996.
Regression Analysis of Electric Energy Consumption of Commercial Buildings In Brazil
Fernando Simon Westphal and Roberto Lamberts, Energy Efficiency in Buildings Laboratory (LabEEE), Federal University of Santa Catarina (UFSC) Florianópolis - Santa Catarina – Brazil Proceedings of Building Simulation 2007.
Properties of NAC and CV[NAC] for Energy Models", Energy Systems Laboratory Technical
Report, Texas A&M University, Ruch, D., May 1993.
Model Identification and Prediction Uncertainty of Linear Building Energy Use Models with Autocorrelated Residuals, ASME Transactions Journal of Solar Energy Engineering, Vol 121,
No.1, pp 63-68, Ruch, D.K., Kissock, J.K and Reddy, T.A., February1999.
Bias In Predicting Annual Energy Use In Commercial Buildings With Regression Models
Developed From Short Data Set, S Katipamula et al., Pacific Northwest Laboratory, November
Uncertainty in Baseline Regression Modeling and in Determination of Retrofit Savings, ASME
Journal of Solar Energy Engineering 120(3): 185-192., Reddy, T., J Kissock, and D Ruch 1998.
The technique of uncertainty and sensitivity analysis is crucial for optimizing sustainable building energy systems, as highlighted in the research conducted by Kotek Petr, Jordán Filip, Kabele Karel, and Hensen Jan from the CTU Technical University in Prague and Technische Universiteit Eindhoven This analysis helps in evaluating performance calculations, enabling engineers to identify key factors that influence energy efficiency and sustainability in building designs By addressing uncertainties, this methodology enhances decision-making processes in the development of energy systems, ultimately contributing to more sustainable architectural practices.
Netherlands Proceedings of Building Simulation 2007.
Metered and Monitored Data Analysis
Investigation of Metered Data Analysis Methods for Commercial and Related Buildings,
McDonald and Wasserman, ORNL, May 1989
A Perspective on Methods for Analysis of Measured Energy Data from Commercial Buildings,
ASME Journal of Solar Energy Engineering 120(3): 150-155., Claridge, D 1998.
Lean Energy Analysis: Identifying, Discovering And Tracking Energy Savings Potential, Kelly
Kissock and John Seryak, Proceedings of Society of Manufacturing Engineers: Advanced
Energy and Fuel Cell Technologies Conference, Livonia, MI, Oct 11-13, 2004.
ASHRAE 1092-RP Final Report: Development of Procedures to Determine In-Situ Performance of Commonly used HVAC Systems, Mingsheng Liu et al., ASHRAE, February 2006.
Final Report Compilation for Impact Assessment Framework, Vernon A Smith, Architectural
Energy Corporation, and Michael Kintner-Meyer, Battelle Memorial Institute, for PIER, October 2003.
The California Evaluation Framework, Prepared for the California Public Utilities Commission and the Project Advisory Group, TecMarket Works et al., June 2004.
California Energy Efficiency Evaluation Protocols: Technical, Methodological, and Reporting Requirements for Evaluation Professionals, Prepared for the California Public Utilities
Commission by TecMarket Works Team, Nick Hall et al., April 2006.
Quality Assurance Guidelines for Statistical, Engineering, and Self-Report Methods for
Estimating DSM Program Impacts, CADMAC Study ID 2001M Berkeley, CA: Pacific
Consulting Services, Ridge, R., D Violette, D Dohrman, and K Randazzo 1997.
Model Energy-Efficiency Program Impact Evaluation Guide (Draft), National Action Plan for
Energy Efficiency, Steve Schiller, July 2007.
Fault Detection and Performance Monitoring
Project 2.5 focuses on utilizing pattern-recognition techniques for fault detection and diagnostics in high-performance commercial building systems Task 2.5.2, led by R.S Briggs from Battelle Northwest Division for PIER in April 2001, emphasizes the selection of effective pattern-recognition methods This initiative aims to enhance the development of whole-building fault management, ensuring improved operational efficiency and reduced energy consumption in commercial buildings.
Detection Methods, Element 5 - Integrated Commissioning and Diagnostics, Project 2.3 -
Advanced Commissioning and Monitoring Techniques, Mingsheng Liu et al., for PIER, June
Model-Based Performance Monitoring: Review of Diagnostic Methods and Chiller Case Study,
Philip Haves and Sat Kartar Khalsa, Lawrence Berkeley National Laboratory, ACEEE 2000 Summer Study on Energy Efficiency in Buildings, Efficiency and Sustainability, August 20-25, 2000.
Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I Srinivas Katipamula, PhD and Michael R Brambley, PhD, HVAC&R Research,
Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II Srinivas Katipamula, PhD and Michael R Brambley, PhD, HVAC&R Research,
A Specifications Guide for Performance Monitoring Systems, Kenneth L Gillespie, Jr et al.,