Advanced Retro-Commissioning Conceptual M&V Project Plan

A Conceptual Plan for Energy-Related Measurement & Verification of Advanced Retro-Commissioning Technology Demonstration Projects Craig Wray, Jessica Granderson, Guanjing Lin, Xiufeng Pang Building Technology and Urban Systems Division Lawrence Berkeley National Laboratory Prepared for: DOE Building Technologies Office June 25, 2015 Page DISCLAIMER This document was prepared as an account of work sponsored by the United States Government While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California The views and opinions of authors expressed herein not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California ACKNOWLEDGEMENT This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology of the U.S Department of Energy under Contract No DE-AC02-05CH11231 Much of the material in this document is derived from or was provided by the International Performance Measurement and Verification Protocol, and documents published by the Federal Energy Management Program (FEMP) of the U.S Department of Energy, and Quantum Energy Services and Technology (QuEST) Page TABLE OF CONTENTS 1.Introduction 2.Measurement and Verification: An Overview .5 3.M&V Options: Summary of Approaches 3.2 Option B: Retrofit Isolation With All Parameter Measurement .10 3.2.1 Approach to Option B 10 3.3 Option C: Whole-Building Data Analysis 10 3.3.1 Approach to Option C 11 3.3.2 Data Collection 11 3.4 Option D: Calibrated Simulation 12 3.4.1 Approach to Option D 13 3.4.2 Simulation Software 14 3.4.3 Model Calibration 14 4.Developing Regression Models for M&V 16 4.1 Independent Variables 16 4.2 Choosing a Model 16 5.Selecting an M&V Approach: IPMVP Options A-D 18 5.1 M&V Considerations for Option A 18 5.2 M&V Considerations for Option B 19 5.3 M&V Considerations for Option C 19 5.4 M&V Considerations for Option D 20 6.Developing an M&V Plan 22 7.Considerations for Technology Demonstration Projects .26 References 27 List of Acronyms 29 Other References 30 Bibliography .33 Appendix A: Site Selection Questionnaire .37 Appendix B: M&V Data Analysis Tool 43 Integration with Universal Translator 44 Modeling Method 45 Uncertainty Method 45 Other Tool Algorithms and Routines 46 Summary 46 Page Introduction Building commissioning is a systematic process that can be used to reduce building energy use and to improve indoor environmental conditions for occupants Retro-commissioning (RCx) is specific to existing buildings and includes identifying performance goals as well as deficiencies and improvement opportunities It also includes implementing changes through tuning, low cost repairs, and more capital-intensive retrofits as needed; and using measurement and verification (M&V) techniques to verify that changes indeed improve building operation Examples of advanced strategies and technologies include using utility data to identify candidate buildings, integrating building automation systems with fault detection tools to inform stakeholders of opportunities, and implementing automated control system adjustments This document describes a conceptual approach for validating the benefits of RCx-related technology innovations relative to current practice, which can be used as a foundation for developing subsequent site-specific M&V plans More specifically, it provides an overview of M&V practices that could be used to assess energy savings associated with implementing these technologies and includes a methodology for comparing post-installation performance to baseline data It also includes project hypotheses, technical objectives, specific indicators of success, and criteria needed to select optimum implementation sites for technology demonstrations Specific criteria include at least the following: facility size and characteristics, number of locations required to develop generalizable conclusions, and required performance data A demonstration site questionnaire that addresses these criteria is included as an Appendix to support follow-on site selection efforts Page Measurement and Verification: An Overview Energy savings themselves cannot be measured directly because they represent the absence of energy use Instead, savings are determined by comparing the energy use before and after the installation of energy conservation measure(s) The “before” case is called the baseline; the “after” case is called the post-installation or performance period Proper determination of savings includes adjusting for changes that affect energy use, but that are not caused by the implemented measure(s) Such adjustments account for changes in weather, occupancy, or other such factors that might differ between the baseline and performance periods Equation describes the general equation used to calculate savings: Savings = (Baseline Energy - Post Installation Energy) ± Adjustments Eqn Equation can be restated so that no adjustments need be made for the post-installation energy measurements In that case, a regression model is developed from baseline energy use measurements and independent variables are used to determine “what baseline energy use would have been” under post-installation conditions Similarly, both baseline and post-installation energy use may be restated to some set of conditions other than baseline or post-installation conditions 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 IPMVP Volume I (2014) provides guidance in the form of a conceptual framework for measuring, computing, and reporting savings achieved by energy or water efficiency projects in buildings It defines key terms and outlines issues that must be considered when developing an M&V plan, but does not provide details for specific measures or technologies Developed through a collaborative effort involving industry, government, financial, and other organizations, the IPMVP document provides four M&V options, and addresses issues related to the use of M&V in third-party-financed and utility projects The FEMP M&V Guideline (2008) contains specific procedures for applying concepts originating in the 2007 version of IPMVP Volume I The Guideline represents a specific application of the IPMVP for federal projects It outlines procedures for determining M&V approaches, evaluating M&V plans and reports, and establishing the basis of payment for energy savings during the contract These procedures are intended to be fully compatible and consistent with the IPMVP document ASHRAE Guideline 14 (2002) is a reference for calculating energy and demand savings associated with M&V activities In addition, it sets forth instrumentation and data management guidelines and describes methods for accounting for uncertainty associated with models and measurements It specifies three engineering approaches to M&V Compliance with each approach requires that the overall uncertainty of the savings estimates be below prescribed thresholds The three approaches presented in Guideline 14 are closely related to and support the options provided in IPMVP Volume I Guideline 14, however, does not discuss issues related to performance contracting Page In general, M&V activities include site surveys, metering energy and independent variables, engineering calculations, and reporting How these activities are applied to determine energy savings depends on characteristics of the energy conservation measures (ECMs) being implemented and balancing accuracy in energy savings estimates with the cost of M&V itself IPMVP Volume I lists four M&V protocol options that enable one to apply a range of suitable techniques for a variety of applications (summarized in Section of this document, with excerpts from Chapter of the FEMP Guideline): • Option A (Retrofit Isolation with Key Parameter Measurement) • Option B (Retrofit Isolation with All Parameter Measurement) • Option C (Whole Building) • Option D (Calibrated Simulation) A simple graphical representation of the savings impact demonstrated through application of M&V is shown in Figures and These data were collected as part of a monitoring-based commissioning project (MBCx) at UC Berkeley’s Soda Hall In this IPMVP Option B approach, energy use by the HVAC systems (chillers, pumps, air handlers) was measured for three months prior to improving the operational efficiency of these systems As Figure shows, a baseline energy model was developed with a simple linear regression of daily energy use versus ambient dry-bulb temperature Figure Scatter plot of daily HVAC energy use vs ambient temperature (2-parameter model) Page The resulting statistical indices (i.e root-mean square error - RMSE) are shown in Figure Ambient temperature and energy use data continued to be collected in the post-installation period, and the baseline energy use projected into the post-installation period using the model Figure shows the savings over the post-installation measurement period as the difference between the projected baseline model and the measured post-installation use Figure is a powerful visualization of the M&V concept, and highlights its strengths in demonstrating savings to project and program sponsors It is simple to understand, and provides several useful insights into the HVAC system energy use: • Demonstrates the dependence of HVAC energy use with ambient temperature • Demonstrates magnitude of energy savings each day • Creates new baselines for additional projects • Compares post-installation model (orange line in post installation period) with postinstallation energy use to provide an indication when energy performance is slipping, and can be used for on-going tracking purposes 4,500 4,000 Baseline Model: kWh = 79.9*OAT + 1129 RMSE = 136 kWh Post-Install Model: kWh = 44.1*OAT - 336 RMSE = 213 kWh 3,500 2,500 2,000 1,500 1,000 Baseline Period Post-Installation Period 500 00 11 /2 00 2/ 13 /2 00 2/ 15 /2 00 2/ 17 /2 00 2/ 19 /2 00 2/ 21 /2 00 2/ 23 /2 00 2/ 25 /2 00 2/ 27 /2 00 3/ 1/ 20 06 3/ 3/ 20 06 3/ 5/ 20 06 3/ 7/ 20 10 06 /3 1/ 20 06 11 /2 /2 00 11 /4 /2 00 11 /6 /2 00 11 /8 /2 11 06 /1 0/ 20 11 06 /1 2/ 20 11 06 /1 4/ 20 11 06 /1 6/ 20 11 06 /1 8/ 20 11 06 /2 0/ 20 11 06 /2 2/ 20 11 06 /2 4/ 20 11 06 /2 6/ 20 11 06 /2 8/ 20 06 2/ 2/ 9/ Daily kWh Use 3,000 Date Break Date HVAC Daily kWh Usage Baseline Post-Install Model Figure Representation of the M&V concept over time Page M&V Options: Summary of Approaches M&V approaches are divided into two general types One type (retrofit isolation) considers only the affected equipment or system independent of what occurs in the rest of the building The other type (whole-building) considers the total energy use and de-emphasizes specific equipment performance IPMVP Options A and B are retrofit isolation methods; Option C is a wholebuilding method; Option D can be used as either, but is usually applied as a whole-building method One primary difference between these approaches is where the boundary of the ECM is defined All energy used within the boundary must be considered The four generic M&V options are summarized in Table Each option has advantages and disadvantages based on site-specific factors and the needs and expectations of the stakeholders Retro-commissioning efforts (RCx) usually focus on HVAC operations (although lighting is sometimes included), with improvements spanning many pieces of equipment, and have been shown to generate median savings of 16% Therefore, IPMVP Options B, C, and D are most useful for quantifying RCx energy savings If lighting measures are implemented, Option A is often used to determine end-use specific savings Page Table 1: IPMVP M&V Options M&V Option Option A: Retrofit Isolation with Key Parameter Measurement Option B: Retrofit Isolation with All Parameter Measurement Performance and Usage Factors This option is based on a combination of measured and estimated factors when variations in these factors are not expected Measurements are spot or short-term and are taken at the component or system level, both in the baseline and post-installation cases Measurements should include the key performance parameter(s) that define the energy use of the ECM Estimated factors are supported by historical or manufacturer’s data Savings are determined by means of engineering calculations of baseline and post-installation energy use based on measured and estimated values This option is based on periodic or continuous measurements of energy use taken at the component or system level when variations in factors are expected Energy or proxies of energy use are measured continuously Periodic spot or short-term measurements may suffice when variations in factors are not expected Savings are determined from analysis of baseline and reporting period energy use or proxies of energy use Option C: Utility Data Analysis This option is based on long-term, continuous, wholebuilding utility meter, or sub-metered energy data Savings are determined from analysis of baseline and reporting period energy data Typically, regression analysis is conducted to correlate with and adjust energy use to independent variables such as weather, but simple comparisons may also be used Option D: Calibrated Computer Simulation Computer simulation software is used to model energy performance of a whole-building (or sub-building) Models must be calibrated with actual hourly or monthly billing data from the building Implementation of simulation modeling requires engineering expertise Model inputs include building characteristics; performance specifications of new and existing equipment or systems; engineering estimates, spot-, short-term, or long-term measurements of system components; and longterm whole-building utility meter data After the model has been calibrated, savings are determined by comparing a simulation of the baseline with either a simulation of the performance period or actual utility data Page Savings Calculation Direct measurements and estimated values, engineering calculations, and/or component or system models are often developed through regression analysis Adjustments to models are not typically required Direct measurements, engineering calculations, and/or component or system models often developed through regression analysis Adjustments to models may be required Based on regression analysis of utility meter data to account for factors that drive energy use Adjustments to models are typically required Based on computer simulation model (such as EnergyPlus) calibrated with whole-building or end-use metered data or both Adjustments to models are required 3.2 Option B: Retrofit Isolation With All Parameter Measurement M&V Option B uses periodic or continuous metering of all energy quantities, or all parameters needed to calculate energy, during the performance period This approach provides the greatest accuracy in the calculation of savings, but increases the performance-period M&V cost Option B is typically used when any or all of the following conditions apply: • For simple equipment replacement projects with energy savings that are less than 20% of total facility energy use as recorded by the relevant utility meter or sub-meter (Option C is not applicable) • When energy savings values per individual measure are desired • When interactive effects can be estimated using methods that 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) 3.2.1 Approach to Option B Option B procedures rely on the physical assessment of equipment change-outs to ensure that the installation meets specifications The potential to generate savings is verified through observations, inspections, and spot/short-term/continuous metering of energy or proxies of energy use (e.g., using variable frequency drive speed as a proxy for motor power) Baseline models are typically developed by correlating metered energy use or proxies with key independent variables Depending on the ECM, spot or short-term metering may be sufficient to characterize the baseline condition, and the continuous metering of one or more variables may occur after retrofit installation It is appropriate to use spot or short-term measurements in the performance period to determine energy savings when variations in performance are not expected, and may support some normalized savings approaches though adjustments to the baseline and/or the performance period model(s) When variations are expected, as in the case of retrocommissioning, it is appropriate to measure factors continuously Continuous monitoring of information also can be used to improve or optimize equipment operation over time, thereby improving the performance of the retrofit 3.3 Option C: Whole-Building Data Analysis Energy savings under Option C are estimated by developing statistically representative models of whole-building or sub-metered energy consumption (i.e., therms and/or kWh) This method confirms total energy savings, but does not measure the savings from individual components In general, Option C should be used with complex equipment replacement and controls projects for which predicted savings are relatively large, i.e., greater than about 10% to 20% of the building’s energy use, on a monthly basis Depending on the building’s predictability, availability of more granular interval meter data, and uncertainty requirements, savings of less than 10% may Page 10 Bibliography General M&V 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 PIER, December 1999 Inverse Model Toolkit 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 Transactions, Volume 109, Part 2, 2003 Inverse Model Toolkit: Application and Testing, Jeff S Haberl et al., ASHRAE Transactions, Volume 109, Part 2, 2003 Page 33 Statistical Modeling 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 Claridge, D 1995 A Development and Comparison of NAC Estimates for Linear and Change-Point Energy Models for Commercial Buildings, Energy and Buildings, Vol 20, pp.87-95, Ruch, D.K and Claridge, D.E., 1993 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 CoolToolsTM Simulation Model, Dudley J Benton et al., ASHRAE Transactions 2002 Development and Testing of the Characteristic Curve Fan Model, Jeff Stein and Mark Hydeman, ASHRAE 2004 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 Kissock et al., March 1993 Monthly Variable-based Degree Day Template: A spreadsheet procedure for calculating a 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 Uncertainty 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 2004 Page 34 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 Technique Of Uncertainty And Sensitivity Analysis For Sustainable Building Energy Systems Performance Calculations, Kotek Petr, Jordán Filip, Kabele Karel, and Hensen Jan, Department of Microenvironmental and Building Services Engineering, CTU Technical University in Prague, Czech Republic, and Building Physics & Systems, Technische Universiteit Eindhoven, 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 Impact Assessment 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 – Pattern-Recognition Based Fault Detection and Diagnostics, Task 2.5.2—Select Pattern-Recognition Techniques, R.S Briggs, Battelle Northwest Division, for PIER, April 2001 High Performance Commercial Building Systems: Development of Whole-Building Fault Detection Methods, Element - Integrated Commissioning and Diagnostics, Project 2.3 - Page 35 Advanced Commissioning and Monitoring Techniques, Mingsheng Liu et al., for PIER, June 2002 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, ASHRAE January 2005 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, ASHRAE April 2005 A Specifications Guide for Performance Monitoring Systems, Kenneth L Gillespie, Jr et al., Lawrence Berkeley National Laboratory, 2007 Page 36 Appendix A: Site Selection Questionnaire Building commissioning is a systematic process that can be used to reduce building energy use and to improve indoor environmental conditions for occupants Retro-commissioning is specific to existing buildings and includes identifying performance goals as well as deficiencies and improvement opportunities; implementing changes through tuning, low cost repairs, and more capital-intensive retrofits as needed; and using measurement and verification (M&V) techniques to verify that changes indeed improve building operation Examples of advanced strategies and technologies include using utility data to identify candidate buildings, integrating building automation systems with fault detection tools to inform stakeholders of opportunities, and implementing automated control system adjustments The following are important considerations for technology demonstration site selection Please provide as much information as possible SITE OVERVIEW 1) Site location (address): _ _ _ _ 2) Climate zone1 and other location-specific weather-related factors: _ _ _ _ _ _ 3) Commercial building type2 (check all that apply for the building):  Education  Lodging/Hospitality   Food Service  Supermarket/Grocery   Hospital  Office   Restaurant  Public Assembly   School  Laboratory   Courthouse  Retail  Fire Order/Safety Worship Warehouse/Distribution Center Bank Multi-Family or Dorm Data Center 4) Annual Hours of Operation / Types and Number of Occupants / Occupancy Schedules: _ _ _ _ 5) Year of Construction / Building Size (square feet) / Number of Floors: http://apps1.eere.energy.gov/buildings/publications/pdfs/building_america/ba_climateguide_7_1.pdf http://www.eia.gov/consumption/commercial/building-type-definitions.cfm and http://www.energystar.gov/buildings/facility-ownersand-managers/existing-buildings/use-portfolio-manager/identify-your-property-type-0 Page 37 _ _ _ _ 6) Building Annual Energy Use and Peak Demand (2013) Electricity (kWh and kW): Natural Gas (cubic feet): _ Total Site Energy (all fuels, Btu per square foot): 7) Heating and Cooling Sources:  Central plant chillers Age: Capacity:  Central plant boilers Age: Capacity:  Direct-expansion (DX) packaged rooftop units Age: Capacity:  District heating/cooling 8) Air Distribution System Types:  Constant Volume (CAV)  Variable-Air-Volume (VAV)  Single-Duct  Dual-Duct Age: Airflow (cfm): _ Age: Airflow (cfm): _ 9) Other technology(ies) under consideration for demonstration at this site (check any that apply):  Advanced Compressor Rack and Refrigerant Systems  HVAC (cooling, heating, ventilation, dehumidification)  Open Refrigerated Display Case Retrofits  Daylighting Systems and Controls  External Shading Attachments  Lead/Lag plug load strategies  Renewables and Distributed Generation  Submetering of building components or panels 10) Is there a commitment by a decision-making authority in the organization to facilitate the project through its conclusion by ensuring the availability of requisite staff and resources? a) _ Yes Provide the following: Name: Title: Organization: Phone: Email: b) No 11) Is there a strong advocate on or near the site who can serve as the primary point of contact, with access to the space, equipment, and operations? Having a local advocate will greatly facilitate addressing potential issues with both the space and its operation and occupants a) _ Yes Provide the following: Name: Title: Page 38 Organization: Phone: Email: b) No 12) Identify any factors below that could obstruct/create difficulty with using the site for a demonstration (check all that apply): a) _ Site operates under energy savings performance contract b) _ Pending changes to physical space or usage that could disrupt the demonstration Typically, the demonstration project needs a consistent test environment for useful preand post-retrofit data collection c) _ Safety or security constraints These may come in the form of building or data access restrictions associated with the nature of the work in the space d) _ Atypical/non-standard electrical layout or configuration (such as modifications that may make measurements difficult) e) _ Limited access for installation of technology and metering f) _ Other (explain): _ Explain the conditions for any of the boxes checked above: 13) Does the site have available historic energy use data? a) _ Yes Describe data period, interval (e.g., monthly, hourly, 15 min), and accessibility (e.g., remotely accessible, archived in spreadsheets or PDF files). _ b) _ No 14) Are you able to access energy cost data? a) _ Yes b) _ No, I don’t have that information 15) Are there any problems in the building (e.g., high energy use, comfort or air quality complaints)? a) _Yes Explain: _ b) _No Explain why the site should still be considered: 16) Do existing controls operate the system properly? a) _Yes b) _No If no, can they be removed or re-configured? i) _Yes Explain how modification or replacement of the controls might affect building operations: _ ii) _No Explain why the site should still be considered: _ Page 39 17) Are design and operation documents available:  As-built drawings  Control sequences of operation and set points  Test and balance reports  Standard operating procedures  Equipment specifications  Operations and maintenance schedules  Commissioning reports 18) Have any renovations or retrofits been carried out since the design documents were prepared? a) _Yes Explain year and scope: b) _No 19) Is there stable space operation and function? Spaces where operations may be changing during the demonstration period should be avoided as this may introduce additional variability a) _Yes b) _No Explain: _ 20) Does the site have an Energy Information System (EIS) or Building Automation System (BAS)? a) _Yes Make and model: _ i) If yes, can the EIS or BAS be accessed remotely? (1) _Yes (2) _No Explain restrictions: _ b) _No 21) Are there any known EIS/BAS limits (e.g., maximum number of points, data collection frequency)? (1) _No (2) _Yes Explain restrictions: _ 22) Does the site have whole-building and submetering through the same EIS/BAS? a) _ Yes b) _ No Explain: 23) Are any standalone data loggers used (e.g., for temperature, electrical power, filter pressure drop)? (1) _No (2) _Yes Explain uses: Page 40 24) Is electric power measurement instrumentation installed that can be used for the demonstration? a) _Yes Describe instrumentation (e.g., power meter types) and data collection process. _ b) _No i) Can electrical panels or junction boxes be made available for metering access along with space for metering equipment? (1) _Yes (2) _No ii) Do you anticipate any conflict with operations of the space or area? (1) _No (2) _Yes iii) Is a qualified electrical worker available to install electric power instrumentation? (1) _Yes (2) _No 25) Is mechanical measurement instrumentation installed that can be used for the demonstration (i.e., air or fluid flow, pressure, relative humidity, speed, temperature sensors)? a) _Yes Describe instrumentation (e.g., sensor types) and data collection process. b) _No i) Can the ducts, piping, mechanical rooms, and roof be made available for instrumentation access along with space for measurement equipment? Explain any access restrictions: ii) Do you anticipate any conflict with operations of the space or area? 26) Is an onsite weather station available? a) _Yes Explain parameters measured (temperature, RH, solar radiation, wind speed and direction): _ b) _No 27) Describe accuracy and calibration frequency of monitoring equipment (EIS/BAS sensors, data logger, weather station) _ Page 41 28) Are any other advanced meters or smart meters used that can provide interval energy data readings? a) _Yes Explain parameters measured and meter types _ b) _No 29) Are existing staff capable of carrying out M&V-related diagnostic tests and data collection? a) _Yes Explain skill level: _ b) _No 30) Other comments or notes: _ Page 42 Appendix B: M&V Data Analysis Tool Traditionally, data that can be used in M&V analysis have been difficult to obtain and come from multiple sources The tasks of collecting data, merging data sets, pre-conditioning and preparing data for analysis are demanding enough to prevent practitioners from performing rigorous M&V Practitioners need statistical analysis or simulation skills, plus software that is capable of manipulating large data sets The time and materials costs to collect the required data for the required duration, prepare and run the analysis, and document the results can exceed the value of the savings obtained if these required resources are not in place M&V implementers also need to assess the ability of the baseline energy model to determine savings prior to finalizing the M&V approach The annual savings must be larger than the uncertainty in the baseline model, in order to use the model effectively Currently “rules of thumb” are used when analysis of the baseline data can validate the approach This frequently results in discarding a perfectly appropriate and cost-effective M&V strategy, or a foregoing an M&V strategy altogether Commercial tools that are available both free of charge, and for licensing fees are increasingly beginning to offer support for ‘project tracking’ and M&V of energy savings These tools may either fully or partially automate the process of creating a baseline model, and projecting the model to determine energy savings For example, an M&V data analysis tool was recently developed by QuEST and integrated into Pacific Gas and Electric’s Universal Translator (2013) The tool allows users to process data from different periods of operation and to determine non-routine adjustments Another feature of the M&V tool is that enables estimation of the baseline model uncertainty as well as the savings uncertainty Baseline model uncertainty indicates how well a particular regression model predicts measured baseline energy data Savings uncertainty indicates the likelihood that the actual savings is within the confidence bounds described by the uncertainty estimate Estimating savings uncertainty using regressions based on time-series data is an evolving field Models developed from ordinary least-squares regressions are found to greatly underestimate uncertainties in both baseline energy as well as savings This is primarily due to the assumption of independence of each data point: that the value of each data point does not depend on any other point In buildings, this assumption does not hold for all points as energy use on an hourly basis does depend on the energy use of preceding hours There is some dependence among points with daily time intervals as well In ASHRAE Guideline 14, an approach based on fractional savings is used While this approach allows ordinary least squares regressions, it makes allowance for the true number of independent data points being less than the actual number in its calculations of savings uncertainty There is a significant research need to develop more robust methods for computing uncertainty in the energy forecasts Recent efforts include fractional savings (ASHRAE 2002) and nearest neighbor (Subbaro et al 2011) approaches Several issues must be addressed in using these methods, including the amount of data required, variations in building energy use not caused by the regressor variables, and data autocorrelation In the meantime, the M&V tool adopts a cross-validation approach to decrease the impact of auto-correlated energy data on uncertainty estimates Among this method’s strengths is that it applies to most model development methods, not only those that assume residuals are normally distributed Cross-validation is a method where a known data set is partitioned into several Page 43 equally sized subsets, and one subset is “held out” from the other data sets while the remaining datasets are used to “train” the statistical models The model’s prediction results for the “held out” dataset (the “prediction” set) are used to calculate the modeling error The process is repeated for all of the partitioned datasets and an average may be used to determine the generalized error of the model This general error term is typical of the amount of data in the subset For the M&V tool, this subset was taken as one month of data, and thus the error is typical of a baseline month How this error term propagates over multiple months, as would be required when calculating savings, is not known however While further research is needed on this approach, the error is limited when using hourly or daily models The M&V tool facilitates assessment of this M&V approach prior to installation of measures Tool users can compare the baseline model uncertainty with expected savings to understand how accurately the method will ultimately estimate savings If the uncertainty exceeds user requirements, they may elect to pursue a different M&V method Providing high-level regression and savings uncertainty analysis in a tool that is integrated with a software platform where data are simple to upload and merge is the approach to reducing overall project costs and improve confidence in the results Making the M&V tool freely available to any user reduces complicated data processing and analysis time Making the project files portable should also speed project review and lessen overall project time PG&E’s UT provides a platform for uploading the data and conducting data quality checks In particular, the UT has wizards that recognize data from different sources and file formats; users need only drag files across the screen to upload the data Attributes of the data files can then be assigned, such as naming files, adding descriptions, and specifying time interval re-sampling rates (i.e., creating hourly or daily time intervals from the raw data) The UI also facilitates data set merging, filtering, applying functions, and charting Each of these functions may be required prior to conducting the M&V analysis All tool charts, data, and model outputs are exportable, so that the data and models may be used in other software or spreadsheets Descriptions of the available functions and features of the UT are available on the website (http://utonline.org/cms/) Integration with Universal Translator The M&V analysis tool was designed according to typical M&V process steps These steps include: data collection, merging, re-sampling, and quality control, which are each functions in the UT Once the data are prepared, the user opens the M&V analysis module to proceed M&V analysis is not completed in one session with the tool Typically, prior to installation of energy efficiency measures, baseline data are collected, prepared, and a baseline model is developed and assessed The assessment compares the uncertainty of the baseline model with the expected savings If the uncertainty is large, project sponsors can make adjustments to the M&V process or decide on an alternate M&V approach The tool allows users to upload baseline data, develop and assess a model for the application, upload and append additional data, develop new models, and so on until a satisfactory model is established Following a project’s installation and waiting for a time to collect post-installation data, the tool allows users to upload, merge, re-sample, and check data quality, and proceed with the M&V analysis All of the raw and processed data and analysis work performed is stored in a project Page 44 file In addition, all raw and processed data and analysis results are available for export to other software Modeling Method There are many choices for developing regression models Users may select a model type, of which there are four: Time and Independent Variable, Independent Variable only, Time Only, or Average Dependent The user selects one of these types based on their own understanding of the major influences and available data that influence energy use in a building For example, for a daily analysis time interval, and a building used continuously throughout the week, a temperature-only model may yield the best fit to the data Other buildings may yield better fits by including time-of-week in the regression The tool allows users to also select the number of linear line segments for the independent variable The choices are: • “Equal size linear segments”, where the range of independent variable data (minimum to maximum) are divided into a user-selected number of equal segments, • “Equal number of samples per segment”, where the user specifies the number of segments and the tool divides the number of data points by this number and establishes line segments with an equal number of data points per segment (line segments will be short for temperature ranges with a lot of data and longer where there are fewer data points) • “Optimize using change point”, where the tool establishes two linear segments and finds the best location (i.e., independent variable point) where the segments meet • “Optimize using change points”, where the tool establishes three linear segments and finds the two best locations (i.e., independent variable point) where the segments meet • “Quadratic”, where the tool assumes a quadratic relationship between energy use and the independent variable The energy data used for the independent variable model type are in the data portion that is not time dependent The tool subtracts out the time dependent portion from the data prior to developing this relationship The two change-point selections allow users to develop fourparameter and five-parameter change-point models (Kissock 2002) for models that not have time dependence Uncertainty Method Uncertainties are estimated using a cross-validation method This method requires the data from which the model is developed to be partitioned into multiple sets, and a model is developed using one set of data to predict the next set The error between the model prediction and the actual data is then determined This process is repeated for each of the sets, and an average of these errors, multiplied by the number of anticipated post-installation months, is taken as the model error In the M&V tool, one month is used as the set unit To have enough error points in the averages (e.g., for the baseline, avoided energy use, normalized savings cases), there must be at least six months of data for each model/case Page 45 For avoided energy use (where savings are reported for the measurement period following an energy savings project), the savings uncertainty will be the number of post-installation months, times the average monthly baseline prediction error For normalized savings, when both baseline and post-installation energy are projected to annual conditions such as typical meteorological year (TMY) temperatures, and the baseline and postinstallation model uncertainties are based on 12 months of prediction and the results are combined through normal propagation of error equations (square root of sum of squares of the error terms) Other Tool Algorithms and Routines The M&V tool allows users to calculate savings under two IPMVP scenarios, which have been mentioned above: avoided energy use and normalized savings Calculating avoided energy use requires a baseline energy model, and independent variable data from the post-installation period Post-installation period data are uploaded into the UT and prepared in the same way as for the baseline data Users select the Post-Installation option in the M&V analysis module to select the dependent and independent variables, and define the postinstallation time period No post-installation model development is required For the “Avoided Energy Use” option, the M&V tool calculates avoided energy use upon selecting that option A line chart shows the adjusted baseline energy use and post-installation use Normalized savings requires a post-installation energy model as well as a baseline energy model Post-installation models are developed in the same way as baseline models, but in the PostInstallation tab instead Once both baseline and post-installation models are developed, the user selects the Normalized Savings option The user then selects the data file that has the conditions to which energy use is adjusted The data file, usually a TMY weather file for temperaturedependent models, must have been previously uploaded into the UT The Normalized Savings and savings uncertainty are then computed Note that this allows savings to be “extrapolated” beyond the measurement period, which technically does not adhere to IPMVP principles, but is a common practice in the industry Summary Complex regression modeling functionality has been programmed into the M&V analysis module Regression models using time as well as an independent variable, usually ambient temperature, have been included in the M&V Tool The user may use menu-driven selections to customize modeling types and apply them to filtered sets of data This allows users to think more about how to model a building properly rather than about the mechanics of the regression process Various charts and graphs allow the users to quickly assess model fit and performance in the development of appropriate models The ability of the M&V tool to set up analysis bins based on filtering the data in different time periods is useful to improve modeling accuracy Occupied and unoccupied periods exhibit very different energy use behavior One tool tester asserted that the tool should allow for automatic filtering of the data, as well as allowing the tool to set up a function to vary the start-stop operation in a building, as many buildings operate in this way Although such features may be useful, they are beyond the scope of the current project It was noted, however, that the users have begun to think about useful tool features that would make it more generally applicable Page 46 Regarding the M&V analysis module, the decision to use a time-and-temperature based model rather than simple change point models was advantageous Testing group members liked the accuracy the tool provides when using hourly time intervals, allowing them to visualize times throughout the day or week when savings are being achieved This aspect of visualizing projected baseline usage along with measured post-installation usage is helpful to owners and service providers to maintain savings over time Tool users also noted that the time-and-temperature model, which was developed by LBNL, has applications in demand response Using 15-minute or hourly analysis time intervals, baseline models can be developed and used to estimate demand reductions when a utility calls a demand reduction event While the M&V tool was not developed specifically for quantifying these benefits, the principles are the same It is noted that future tool developments could include demand response applications Overall, the M&V tool provides users with a means to develop a rigorous M&V analysis in a streamlined process It provides advanced regression modeling capability, and allows users to test multiple scenarios to get the best energy models It also enables users to focus on the M&V approach and uncertainties in baseline modeling and savings at key points in an energy efficiency project, rather than on the actual analysis required to prepare data Page 47 ... choosing the M&V options and techniques to use for each project • Value of ECM in Terms of Projected Savings and Project Costs: M&V efforts should be scaled to the value of the project so that... 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. .. related to the M&V plan In addition, savings could be quantified beyond the M&V phase This information could be useful for allocating costs among different tenants, planning future projects, or