M&V 2.0: A User s Guide

Transcription

1 M&V 2.0: A User s Guide West Coast Energy Management Congress June 8, 2017 David Jump, Ph.D., P.E. kw Engineering djump@kw-engineering.com 1

2 Agenda What is M&V 2.0? Process Steps & Tools Non-Routine Events Benefits and Risks of M&V 2.0 Uses & Case Studies

3 What is New About M&V 2.0? What isn t New? M&V 2.0 tools are built upon savings estimation techniques that have been used for decades Whole-building and submeter-based pre/post (Option C) Retrofit Isolation (Option B) What s new is: Degree of automation in data acquisition, and model creation Granularity and volume of data can improve quality of result Potential for continuous feedback Integration of M&V capability with other analyses for operational efficiency Software as a service offerings for owners, managers, program administrators

4 Option C: Whole Facility Data Sources: Utility bills Local weather stations Occupancy Schedules Production Rates kwh save = kwh base T post kwh post (T post )

5 M&V 1.0 Monthly Data Linear regressions 12 months/data points per year Less Accuracy 12 mo. monitoring duration

6 M&V lnterval Data Advanced analytics 8760 hourly/365 daily points per year More Accuracy Shorter monitoring duration: 3 to 6 months Applicable to subsystems (Option B)

7 M&V 2.0 High frequency data & advanced analytics Rapidly process data and visualize key information Whole Building (Option C) Building Subsystem (Option B) Boiler VSD Uses: Building Audits / Commissioning / M&V / Performance Tracking

8 Advanced Analytics Familiar Linear OLS Regression More Advanced ASHRAE RP1050 Change-Point Models LBNL Temperature and Time-of-Week Model Exotic Neural Networks Nearest Neighbor Machine Learning Much More..

9 Comparison Linear OLS Model Temperature only Advanced Model Temperature and Time-of-Week

10 Project Level M&V Tools Public Domain ASHRAE RP1050 Change Point Models Energy Explorer Energy Charting and Metrics Tool (Excel add-in) m/ LBNL TTOW Model Universal Translator, v3 Proprietary BuildingIQ FirstFuel Gridium More! Validation with test data sets and protocols (LBNL) ations/accuracyautomated-measurement Various R, Python M&V Code Transparent/Repeatable

11 Process

12 Metering Concern: Meter Accuracy Bias Measurement Error - eliminate Random Measurement Error reduced as more data used Energy Source Type Typical Accuracy Common Mfgrs Electric Solid state ± 0.2% of reading Square D Eaton Natural Gas Positive displacement ± 1-2% of reading Dresser American CHW/HHW Temperature sensors: solid state Flow meter: turbine, electromagnetic, ultrasonic, or vortex Temp sensors: ± 0.15 F from F Flow meter: ± 0.2% to ± 2.0% per flow meter Calculator accuracy: within ± 0.05% Onicon Flexim Steam Flow: Vortex shedding Temperature: RTD Mass flow: ± 2% of mass flow calculation Rosemount Yokogawa Mfgr s product test results, installed meter calibration reports, submitted with the documentation for all meters.

13 Coverage Factor Good models have maximum range of energy and temperature values Rule: Don t extrapolate 10% beyond max or min baseline temperatures Coverage factor determines how much data to collect prior to project install

14 Develop and Assess Models - I Goodness-of-Fit and Accuracy Metrics Baseline Models NMBE (bias error) < 0.5% CV(RMSE) (random error) < 25% R 2 (independent variables check) > 0.7 Linear Model, CV = 25% TTOW Model, CV = 11%

15 Develop and Assess Models - II Assess Uncertainty E E save, m save. m t n CV 1 ' ' n n m F 1/ 2 Table shows acceptable CV for target savings F, if: E save E save < 10% (90% CI) U < 10% Uncertainty (90% CI) CV(RMSE) % savings 2% 11% 22% 33% 39% 44% 56% 4% 6% 11% 17% 19% 22% 28% 6% 4% 7% 11% 13% 15% 19% 8% 3% 6% 8% 10% 11% 14% 10% 2% 4% 7% 8% 9% 11% 12% 2% 4% 6% 6% 7% 9% 14% 2% 3% 5% 6% 6% 8% 16% 1% 3% 4% 5% 6% 7% 18% 1% 2% 4% 4% 5% 6% 20% 1% 2% 3% 4% 4% 6%

16 Monitor Savings

17 Non-Routine Events 7,000 6,000 Prefilters and bags changed Daily kwh 5,000 4,000 3,000 SF-1 Fails. The other fans ramp up to meet setpoint. SF-1 repaired. Fans return to normal operation Deg. F 2, , /13/2006 4/17/2006 4/21/2006 4/25/2006 4/29/2006 5/3/2006 5/7/2006 5/11/2006 5/15/2006 5/19/2006 5/23/2006 5/27/2006 5/31/2006 6/4/2006 6/8/2006 6/12/2006 6/16/2006 Date AHU-3 Supply Fans Avg. Daily Temp.

18 Non-Routine Adjustments Process Identify the NRE (visualize data or owner report) Determine if NRE Impact is Material (if not, stop) Assess Temporary or Permanent? Constant or Variable Load? Added or Removed Load? Quantify Impact Engineering calcs + assumptions (low quality/cost) Engineering calcs + logged data (med-high quality/cost) Analysis of before/after NRE using metered data (high quality/low cost) Adjust Savings Estimate

19 Non-Routine Adjustment 7, , , Daily kwh 4,000 3,000 Difference ~ 500 kwh Deg. F 2, , /13/2006 4/17/2006 4/21/2006 4/25/2006 4/29/2006 5/3/2006 5/7/2006 5/11/2006 5/15/2006 5/19/2006 5/23/2006 5/27/2006 5/31/2006 6/4/2006 6/8/2006 6/12/2006 6/16/2006 Date AHU-3 Supply Fans Avg. Daily Temp.

20 kwh Deg F Avoided Energy Use (actual savings) 950 Baseline Period Post Install Period `, Source: Universal Translator v3

21 Normalized Savings Reduces risk of extreme weather years

22 Case Study - Pay For Performance 1 2 3

23 Cumulative savings - continuous tracking & feedback

24 Therms/day Therms/day Energy Diagnostics 1. Software identifies high natural gas usage, especially on weekends. 2. Trend review uncovers AC units running continuously over the weekend Actual Expected Actual 3. AC units rescheduled Estimated Savings: (Annual) 65,000 kwh 14,000 therms $20,

25 M&V Documentation M&V Plan Describe Model Why chosen? Mathematical form Independent variables Baseline Period Coverage factor Goodness-of-fit statistics Uncertainty Assessment Calculations How often & how savings are reported Non-routine adjustments More!

26 Best Applications Project Level M&V Predictable buildings, systems Weather sensitive, regularly scheduled Multiple and interactive ECMs Affecting multiple building systems (HVAC, lighting, etc.) Deep savings projects Savings are above the noise Data useful for other purposes Anomaly detection, Performance drift

27 What are the Potential Benefits of M&V 2.0? What is the Value Proposition? Increase visibility, quickly obtain ongoing and interim results feedback Increase savings and enhance customer experience? Improve transparency and trustworthiness of EE savings? Automate parts of the process that computers do well, streamline data acquisition and processing Reduce time and cost to quantify savings? Maintain/improve accuracy in savings? Increase throughput, number of projects going through the pipeline?

28 Risks and Issues Sub Meter Calibration Requirements & Frequency Complex Analysis Methods Not simple OLS anymore! Unpredictable buildings Prescreening may be required Non-Routine Events Added building loads, major occupancy shifts Must remove impacts from savings estimations Data accessibility and security (not covered)

29 Thank You!

30 Predict/Forecast Good buildings: Predictable operation The Good Bad buildings Requires intervention? The Bad Ugly buildings Cannot predict future use The Ugly