Forecasting and Verifying 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 Enhanced Spreadsheet M&V Tool Developed for BPA

Contents Overview of tool Overview of tool Demonstrate all tool features, focusing on the new/enhanced features Demonstrate all tool features, focusing on the new/enhanced features Discuss tool limited support Discuss tool limited support Brainstorm additional uses of the tool Brainstorm additional uses of the tool Brainstorm needs for additional M&V tools Brainstorm needs for additional M&V tools

Enhancements Discussed 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

Need for this Tool

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.

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

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

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

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

Tool Introduction: Worksheets Instructions Instructions User Interaction User Interaction BillingDataBillingData WthrQueryWthrQuery WthrDataWthrData PastProjectsDataPastProjectsData HDDbase (new)HDDbase (new) Outputs Outputs ForecastSavingsForecastSavings VerifiedSavingsVerifiedSavings Background Calculations Background Calculations PastProjectsData ScheduleData (new) Calcs RegressionBase RegressionPost

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.

Tool Calculation Approach Based on ASHRAE Guideline Annex D, Regression Techniques Based on ASHRAE Guideline Annex D, Regression Techniques Now uses a weighted regressionNow uses a weighted regression Now uses variable-base degree-daysNow uses variable-base degree-days Regression Variables Regression Variables Independent Variable is Average Heating Degree-Hours per Day during billing periodIndependent Variable is Average Heating Degree-Hours per Day during billing period Dependent Variable is Average kWh per Day during billing periodDependent Variable is Average kWh per Day during billing period Now user can pick base temperature after evaluation of fit statistics for a list of different base temperaturesNow user can pick base temperature after evaluation of fit statistics for a list of different base temperatures Variable base degree-hours automatically calculatedVariable base degree-hours automatically calculated

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.

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

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 of TMY3 data is not included. Uncertainty associated with the appropriateness of TMY3 data is not included.

Tool Demo

Additional Uses of the Tool

Additional M&V or other Tools

Thank You Bill Koran Quantum Energy Services & Technologies Mira Vowles Bonneville Power Administration

Statistics and Uncertainty