1. Forecasting and Verifying the Energy Savings for Web-Enabled Thermostats in Portable Classrooms: William E. Koran, P.E. Quantum Energy Services and Technologies Mira Vowles, P.E. Bonneville Power Administration A Spreadsheet M&V Tool Developed for BPA

2. Contents Need for this tool Need for this tool IPMVP Option C IPMVP Option C Tool Introduction and Demo Tool Introduction and Demo Forecasting Forecasting Statistics and Uncertainty Statistics and Uncertainty Potential Enhancements Potential Enhancements Comments, questions, additional ideas for enhancements Comments, questions, additional ideas for enhancements

3. Need for this Tool

4. Measurement and Verification Definition M&V is the process of using measurement to reliably determine actual savings. M&V is the process of using measurement to reliably determine actual savings. Verification of the potential to generate savings should not be confused with M&V. Verification of the potential to generate savings does not adhere to IPMVP since no site energy measurement is required. Verification of the potential to generate savings should not be confused with M&V. Verification of the potential to generate savings does not adhere to IPMVP since no site energy measurement is required. The intent of this tool is to provide true M&V. The intent of this tool is to provide true M&V.

5. Visualization of Savings Chart is similar to IPMVP Figure 1, Example Energy History BaselinePost

6. IPMVP Savings Reporting Options Reporting Period Basis (“Avoided Energy Use”) Reporting Period Basis (“Avoided Energy Use”) Baseline is Projected to Reporting Period ConditionsBaseline is Projected to Reporting Period Conditions Avoided Energy Use = Projected Baseline Energy Use minus Actual Reporting Period Energy UseAvoided Energy Use = Projected Baseline Energy Use minus Actual Reporting Period Energy Use Fixed Conditions Basis (“Normalized Savings”) Fixed Conditions Basis (“Normalized Savings”) Baseline and Post period energy use are Projected to a set of fixed conditionsBaseline and Post period energy use are Projected to a set of fixed conditions Normalized Savings = Projected Baseline Energy Use minus Projected Post Energy UseNormalized Savings = Projected Baseline Energy Use minus Projected Post Energy Use

7. IPMVP Option C  Whole Facility Savings are determined by measuring energy use at the whole facility level. Savings are determined by measuring energy use at the whole facility level. Most commonly, utility meter data is used for the energy use measurement. Most commonly, utility meter data is used for the energy use measurement. Routine adjustments are required, such as adjustments for weather conditions that differ between pre-and post. Routine adjustments are required, such as adjustments for weather conditions that differ between pre-and post. Routine adjustments are often made using regression analysis Routine adjustments are often made using regression analysis

8. Approach Taken by this Tool This Tool Uses a Fixed Conditions Basis. This Tool Uses a Fixed Conditions Basis. The Energy Use is projected for a typical year, using TMY3 weather data. The Energy Use is projected for a typical year, using TMY3 weather data. Routine adjustments are made using regression analysis Routine adjustments are made using regression analysis

9. Tool Introduction: Worksheets Instructions Instructions User Interaction User Interaction BillingDataBillingData WthrQueryWthrQuery WthrDataWthrData Outputs Outputs ForecastSavingsForecastSavings VerifiedSavingsVerifiedSavings Background Calculations Background Calculations PastProjectsData Calcs RegressionBase RegressionPost

10. Tool Introduction: Calculation Approach Based on ASHRAE Guideline 14-2002 Measurement of Energy & Demand Savings, Annex D, Regression Techniques Based on ASHRAE Guideline 14-2002 Measurement of Energy & Demand Savings, Annex D, Regression Techniques Independent Variable Independent Variable Average Heating Degree-Hours per Day during billing period (base 65 ºF)Average Heating Degree-Hours per Day during billing period (base 65 ºF) Dependent Variable Dependent Variable Average kWh per Day during billing periodAverage kWh per Day during billing period

11. Tool Introduction: Weather Data Web Query of Hourly Temperatures for Nearest Site Web Query of Hourly Temperatures for Nearest Site Heating Degree-Hours are Calculated for Each Billing Period, divided by 24, and divided by the number of days in the billing period. Heating Degree-Hours are Calculated for Each Billing Period, divided by 24, and divided by the number of days in the billing period.

12. Tool Demo

13. Forecasting Savings For Proposed Projects Weather-dependent load is assumed to have the same relationship (slope) as past projects. Weather-dependent load is assumed to have the same relationship (slope) as past projects. Non-weather-dependent load is assumed to be proportional to number of scheduled hours. Non-weather-dependent load is assumed to be proportional to number of scheduled hours. Uncertainty Uncertainty uncertainty in the baseline regressionuncertainty in the baseline regression uncertainty in the post regression from past projectsuncertainty in the post regression from past projects uncertainty due to variation in the past projects.uncertainty due to variation in the past projects.

14. Statistics and Uncertainty International Performance Measurement and Verification Protocol, Volume 1, 2009. International Performance Measurement and Verification Protocol, Volume 1, 2009. ASHRAE Guideline 14-2002, Measurement of Energy and Demand Savings, 2002, Annex B. ASHRAE Guideline 14-2002, Measurement of Energy and Demand Savings, 2002, Annex B. CCC: Guidelines for Verifying Existing Building Commissioning Project Savings, Using Interval Data Energy Models: IPMVP Options B and C, 2008. CCC: Guidelines for Verifying Existing Building Commissioning Project Savings, Using Interval Data Energy Models: IPMVP Options B and C, 2008. National Institute of Standards and Technology. The NIST Engineering Statistics Handbook, http://www.itl.nist.gov/div898/handbook/index.htm National Institute of Standards and Technology. The NIST Engineering Statistics Handbook, http://www.itl.nist.gov/div898/handbook/index.htm http://www.itl.nist.gov/div898/handbook/index.htm

15. Statistics and Uncertainty BPA Regression Reference Guide (in revision) BPA Regression Reference Guide (in revision) Sections of Particular Relevance: Sections of Particular Relevance: Requirements for RegressionRequirements for Regression Validating ModelsValidating Models Statistical Tests for the Model Statistical Tests for the Model Statistical Tests for the Model’s Coefficients Statistical Tests for the Model’s Coefficients Additional Tests Additional Tests Plus, Tables of Statistical Measures

16. Statistics and Uncertainty T-statistic T-statistic The t-statistic is a measure of the statistical significance of a model’s coefficient. If it is greater than the comparison “critical” t-statistic, the coefficient is significant.The t-statistic is a measure of the statistical significance of a model’s coefficient. If it is greater than the comparison “critical” t-statistic, the coefficient is significant. Critical t-statistics are a function of the required (input) confidence level and the number of data points. For 24 data points, and a 90% confidence level, the critical t-statistic is 1.72Critical t-statistics are a function of the required (input) confidence level and the number of data points. For 24 data points, and a 90% confidence level, the critical t-statistic is 1.72

17. Statistics and Uncertainty Confidence Intervals Confidence Intervals Confidence intervals are a measure of the uncertainty of the regression line.Confidence intervals are a measure of the uncertainty of the regression line. The uncertainty in the savings is dependent on the regression uncertainty.The uncertainty in the savings is dependent on the regression uncertainty. The confidence intervals are a function of the t-statistic.The confidence intervals are a function of the t-statistic.

18. Verified Savings Uncertainty Meter data measurement uncertainty is assumed to be zero. Meter data measurement uncertainty is assumed to be zero. Uncertainty of baseline and post regressions are included. Uncertainty of baseline and post regressions are included. Uncertainty associated with the appropriateness TMY3 data is not included. Uncertainty associated with the appropriateness TMY3 data is not included.

19. Potential Enhancements Use a weighted regression. Use a weighted regression. Adjust the regression for summer occupancy. Adjust the regression for summer occupancy. Limit baseline to whole years. Limit baseline to whole years. Input project start and end dates (use 2 dates). Input project start and end dates (use 2 dates). Use Heating Degree-Hours for Forecast Savings as well as Verified Savings. Use Heating Degree-Hours for Forecast Savings as well as Verified Savings. Use variable-base heating degree-hours. Use variable-base heating degree-hours. Adjust heating degree-hours for the occupancy schedule. Adjust heating degree-hours for the occupancy schedule. Incorporate more completed projects in the forecasting. Incorporate more completed projects in the forecasting. Protect cell formatting. Protect cell formatting. Allow multiple weather sites in WthrData Allow multiple weather sites in WthrData Add capability to benefit from interval meter data Add capability to benefit from interval meter data

20. Comments and Questions

21. Thank You Bill Koran Quantum Energy Services & Technologies 503-557-7828 wkoran@quest-world.com wkoran@quest-world.com 503-230-4796 mkvowles@bpa.gov Mira Vowles Bonneville Power Administration 503-230-4796 mkvowles@bpa.gov mkvowles@bpa.gov

22. Statistics and Uncertainty

23. p-value p-value The p ‑ value is the probability that a coefficient or independent variable is not significantly related to the dependent variable.The p ‑ value is the probability that a coefficient or independent variable is not significantly related to the dependent variable. Rather than requiring an input confidence level as for the t ‑ statistic, the p ‑ value provides probability as an output.Rather than requiring an input confidence level as for the t ‑ statistic, the p ‑ value provides probability as an output.