Truing-Up to Billing Data

Transcription

1 Truing-Up to Billing Data The Imperative for Accurate Software Estimations Dave Beaulieu- CSG Ethan MacCormick- PSD

2 Presenters Ethan MacCormick Dave Beaulieu

3 Description: This session will provide concrete procedures and examples for truing-up home performance energy savings estimates. Softwarebased home performance savings estimates have consistently been overestimated, this presentation will provide real software solutions, then discuss programmatic imperatives to model real-world savings. Objectives: By attending this session, attendees will: Learn quick techniques and best practices for truing-up their energy models to actual energy consumption history to increase savings prediction accuracy. Learn how and why model calibration can save the contractor time building their energy model. Understand how the ANSI/BPI-2400 standard supports model calibration and improved accuracy of energy savings estimates.

4 Agenda What is Truing-up? Why is Truing-up Important? How is Truing-up Accomplished?

5 What is Truing-up? Truing-up is the calibration of energy modeling software to usage data, which improves accuracy of the savings estimates Whole-building energy simulation in which the inputs are adjusted so that the modeled pre-retrofit energy consumption is within an acceptable range of the weather-normalized energy consumption - ANSI/BPI-2400

6 Why Truing-up is Important? Increase confidence in savings predictions Improve Program savings realization rates Can make a better model

7 Why Calibrate Your Model? Prevents over/under predictions of whole workscope savings Can increase accuracy of individual ECM savings predictions Provides feedback to auditor to verify their assumptions Can make modeling easier and quicker

8 Does Calibration Work? NYSERDA study of five years of HP data found that: Lack of model calibration was the most significant factor to account for poor realization rates Retrospective calibration of the HP jobs improved the RR to 90+%

9 Disaggregated Pre-Retrofit Energy Usage Base load Heating Cooling energy time

10 Baseline Model Compared to Disaggregated Pre-Retrofit Usage (Before Model Calibration) Baseline Model and Utility Bills Weather Normalized Base load Heating Cooling Baseline Pre-Retrofit Model energy time

11 Baseline Model Compared to Disaggregated Pre-Retrofit Usage (After Model Calibration) Baseline Model and Utility Bills Weather Normalized Base load Heating Cooling Baseline Pre-Retrofit Model energy time

12 Bills-over-Bills Savings Baseline and Post-Retrofit Usage Weather Normalized Pre-retrofit, Baseline Usage Post-retrofit Usage energy Savings time

13 Impact of Uncalibrated Model on Realization Rates Baseline and Post-Retrofit Usage and Model Weather Normalized energy Pre-retrofit, Baseline Usage Post-retrofit Usage Post-Retrofit Energy Prediction Uncalibrated Baseline Pre-Retrofit Model Poor Realization Rate (X/Y) X Y time

14 Impact of Calibrated Model on Realization Rates Baseline and Post-Retrofit Usage and Model Weather Normalized energy Pre-retrofit, Baseline Usage Post-retrofit Usage Adj Post-Retrofit Energy Prediction Calibrated Baseline Pre-Retrofit Model Good Realization Rate (X/Y) X Y time

15 Basic Calibration Process Create Model per Audit Adjust model to match bills Calculate/disaggregate actual consumption Compare model consumption to actual bills

16 An Accurate Model Requires Good Software Motivated Smart Auditor/Analyst Meaningful Audit Data Actual Energy Usage Data A helpful occupant A supportive business model

17 The Ideal Energy Model Forward Modeling Inverse Modeling Calibrated Model

18 Software Standards: BESTEST & RESNET BESTEST was developed by NREL for stress testing the building load prediction accuracy of software. RESNET Software Accreditation includes BESTEST and adds tests for HVAC, duct losses, DHW, and interaction of multiple improvements. Infiltration Uninsulated and insulated slab Wall and ceiling R-Value Uninsulated and insulated basement Glazing physical properties, area, and Passive solar design orientation Heating and cooling equipment Overhangs Duct distributions in unconditioned Internal loads spaces Exterior surface color DHW equipment Crawl space Savings interactions

19 Appropriate Level of Accuracy Why are you modeling? New home rating Existing home improvements Modeling detail vs. time investment Calibrated TREAT model w improvements = 30 min Skill and judgment of modeler Windows to nearest ¼ inch? Modeling mistakes

20 Appropriate Detail for Client Depends on the customer give me data! just fix my house! Who s your customer base? Important to understand to maximize sales

21 Appropriate Accuracy What is the purpose of model? Software standards Occupants How occupants affect energy use What occupants want to know from model Calibration Software starting to incorporate calibration tools

22 Basics of Calibration Disaggregate weather-normalized billing data by end-use Compare with baseline model end-uses If significant discrepancies, first look for errors/unrealistic entries Then calibrate model Adjust baseload (lights, appliances, DHW) Then adjust heating and cooling (setpoints, HVAC efficiency)

23 Best Practice Modeling includes Calibration Model according to what you KNOW (blower door, surface areas, measured SSE, occupancy, etc.) Compare against the KNOWN Gold Standard (energy bills) Adjust the UNKNOWNS to align the model to the bills. Usage patterns, internal temperatures, R-values, etc.

24 Example of Uncalibrated Model This model will: Over-predict Cooling Electricity savings Over-predict Heating Gas savings Under-predict DHW Gas savings Note: ECMs on Baseload Electricity may or may not be affected

25 Disaggregation is Key to Calibration Disaggregation by fuel type, by enduse enables model output comparison to bills. Quality of bill data regression is very important ANSI/BPI-2400 covers this

26 Model Calibration Feedback to Auditor Which parameters need to be changed? Verify the parameters you directly observe or measure (knowns) Make conservative assumptions about the things you are uncertain of (e.g. insulation coverage in walls, average temp for entire heating season)

27 No Monthly Bills? Calibrate to Annual Usage!! Bounding your baseline model to annual values is still very effective. Get best estimate of annual fuel and electricity usage or cost from the owner TREAT Billing Import Tool converts annual usages into monthly bills ANSI/BPI-2400 has procedure for annual calibration. Disaggregated end-uses calculated from the end-use ratios from your model times the annual fuel usage. Heating and cooling are adjusted by annual HDD and CDD ratios

28 Example: 1980 All-Elec Ranch High use home Model overestimates heating & cooling, underestimates baseload Upgrades: Attic & Crawl Insul, Air Sealing, ASHP Projected Savings: $1,981/year

29 Example: 1980 All-Elec Ranch

30 Example: 1980 All-Elec Ranch Calibrate Baseload - raise plug loads and DHW Calibrate Heating & Cooling - adjust setpoints Calibrated Projected Savings: $1,617/year

31 Calibration Best Practices: Billing Data Make sure billing history disaggregation is of sufficient quality ANSI/BPI-2400 has two calibration paths to cover sufficient bill data quality (eg 12 months gas) and insufficient (eg oil deliveries) If billing history looks anomalous, get feedback from owner Away on vacation? Kids home for the summer? Use software to perform the billing data analysis Make sure end-use disaggregation makes sense All electric homes often show very high baseload with little heating and cooling. Regression analysis can only do so much with monthly bills

32 Calibration Best Practices: Spaces/Zones Model all conditioned spaces as one zone Model all attic spaces as one attic Model all unconditioned basements and crawlspaces as one unconditioned space Garage? Maybe if conditioned space above it

33 Calibration Best Practices: Walls Reduce the above-grade walls of conditioned space to one wall per cardinal direction Reduce all below-grade walls to one wall Exceptions: Want to improve certain sections (e.g. adding insulation) Significantly different construction (e.g. R-5 vs R-13) Be conservative on surface R-values they are better than you think (check ANSI/BPI-2400 Input Constraints) Actual Footprint Actual Footprint vs. Model Walls Model Walls

34 Calibration Best Practices: Temperature Settings Set points vs. average seasonal house temperatures Setbacks and setups may reflect typical house use better. Calibration using single temp is easier.

35 Calibration Best Practices: Checks Review time series charts: Whether the equipment is capable of meeting the load? Correct fuel associated with end-use Correct weather-dependent and weather-independent behavior Reasonable peak monthly usage

36 Calibration Best Practices: Checks Geometry checks Floor area of conditioned space = ceiling area of conditioned space + 10% Window area in conditioned space falls in range (ref Building America papers, 10% - 40% of floor area)

37 Lessons from Calibration An energy rating is not the same as an energy audit Standardization of input parameters (used in ratings) causes errors in savings predictions High-use houses in particular Cooling paybacks seriously overestimated in heating climates and heating overestimated in cooling climates Lighting, appliance and hot water savings underestimated in all climates

38 Incorporating Calibration into Programs Necessary due to significant impact on savings estimates Barriers include Obtaining utility bills Calibration abilities of software Learning calibration method But these barriers are surmountable

39 Click to download the ANSI/BPI-2400-S-2012 Standard.

40 1. Test energy bill data quality 2. Build the pre-retrofit operational model, respecting input constraints 3. Compare model to energy bill data Overview of ANSI/BPI-2400 Standard 4. Make input adjustments until base model passes calibration acceptance criteria 5. Apply proposed improvement measures to the calibrated pre-retrofit operational model 6. Determine qualified operational savings 7. Determine qualified asset-based savings (if desired)

41 ANSI/BPI-2400 Process Flow

42 ANSI/BPI-2400 Input Constraints

43 Monthly Billing Data Quality Correct for estimated reads and outliers Data must span a sufficient portion of the heating/cooling seasons Regress data against outside dry bulb temperature Acceptance limit for the bill regression is a CV-RMSE of <=20%

44 Monthly Usage Data Quality

45 Annual Usage Data Quality Typically for delivered fuels Minimum of two deliveries Must span one full year If records of fuel tank level are sufficient to meet the monthly usage acceptance criteria, then detailed calibration can be used.

46 ANSI/BPI-2400 Calibration Paths Detailed Calibration A model that successfully passes detailed calibration criteria will typically have less uncertainty in the savings predictions than that of the simplified calibration Detailed calibration shall be used when qualified monthly utility data are available for one or more fuel types Simplified Calibration Utility bills do not meet the monthly acceptance criteria Have delivered bulk fuels, typically

47 Summary Calibration (True-up) improves results Energy savings predictions State/Utility program effectiveness Higher Customer confidence Standard Methods and Best Practices ANSI/BPI-2400 standard Non-software specific data quality checks Model Calibration makes better modelers Adjusting what needs to be adjusted Building science detective skills

48 Summary Common Program Links: Contractor Support Site Home Performance Webinars - CRM Section 7 Operational Procedures 2/ contractor-resource-manual/section-7- operational-procedures/ Section 7.4 Eligible Measures List Section 7.6 Effective Useful Life of Measures

49 Questions?

50 Thank You!