forecasting and verifying the energy savings for web-enabled thermostats in portable classrooms:...
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Forecasting and Verifying theForecasting and Verifying theEnergy Savings forEnergy Savings for
Web-Enabled ThermostatsWeb-Enabled Thermostatsin Portable Classrooms:in Portable Classrooms:
William E. Koran, P.E.Quantum Energy Services and Technologies
Mira Vowles, P.E.Bonneville Power Administration
A Spreadsheet M&V ToolA Spreadsheet M&V ToolDeveloped for BPADeveloped for BPA
ContentsContents Need for this toolNeed for this tool IPMVP Option CIPMVP Option C Tool Introduction and DemoTool Introduction and Demo ForecastingForecasting Statistics and UncertaintyStatistics and Uncertainty Potential EnhancementsPotential Enhancements Comments, questions, additional ideas Comments, questions, additional ideas
for enhancementsfor enhancements
Need for this ToolNeed for this Tool
Measurement and Verification Measurement and Verification DefinitionDefinition
M&V is the process of using measurement to M&V is the process of using measurement to reliably determine actual savings.reliably determine actual savings.
Verification of the potential to generate savings Verification of the potential to generate savings should not be confused with M&V. Verification of should not be confused with M&V. Verification of the potential to generate savings does not adhere the potential to generate savings does not adhere to IPMVP since no site energy measurement is to IPMVP since no site energy measurement is required.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 SavingsVisualization of SavingsChart is similar to IPMVP Figure 1,
Example Energy History
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0Ave
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Electricity Use History and Adjusted Baseline
Actual Baseline Data
Actual Post Data
Modeled Baseline
Baseline Post
IPMVP Savings Reporting OptionsIPMVP 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 Avoided Energy Use = Projected Baseline Energy Use minus Actual Reporting Period Energy Useminus 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 Baseline and Post period energy use are Projected to a
set of fixed conditionsset of fixed conditions
• Normalized Savings = Projected Baseline Energy Use Normalized Savings = Projected Baseline Energy Use minus Projected Post Energy Useminus Projected Post Energy Use
IPMVP Option C IPMVP Option C Whole Facility Whole Facility
Savings are determined by measuring Savings are determined by measuring energy use at the whole facility level.energy use at the whole facility level.
Most commonly, utility meter data is used Most commonly, utility meter data is used for the energy use measurement.for the energy use measurement.
Routine adjustmentsRoutine adjustments are required, such as are required, such as adjustments for weather conditions that adjustments for weather conditions that differ between pre-and post.differ between pre-and post.
Routine adjustments are often made using Routine adjustments are often made using regression analysisregression analysis
Approach Taken by this ToolApproach 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 The Energy Use is projected for a typical
year, using TMY3 weather data.year, using TMY3 weather data. Routine adjustments are made using Routine adjustments are made using
regression analysisregression analysis
Tool Introduction: WorksheetsTool Introduction: Worksheets
InstructionsInstructions User InteractionUser Interaction
• BillingDataBillingData
• WthrQueryWthrQuery
• WthrDataWthrData
OutputsOutputs• ForecastSavingsForecastSavings
• VerifiedSavingsVerifiedSavings
Background Background CalculationsCalculations• PastProjectsDataPastProjectsData
• CalcsCalcs
• RegressionBaseRegressionBase
• RegressionPostRegressionPost
Tool Introduction:Tool Introduction:Calculation ApproachCalculation Approach
Based on ASHRAE Guideline 14-2002Based on ASHRAE Guideline 14-2002Measurement of Energy & Demand Savings,Measurement of Energy & Demand Savings,Annex D, Annex D, Regression TechniquesRegression Techniques
Independent Variable Independent Variable • Average Heating Degree-Hours per Day during Average Heating Degree-Hours per Day during
billing period (base 65 ºF)billing period (base 65 ºF)
Dependent Variable Dependent Variable • Average kWh per Day during billing periodAverage kWh per Day during billing period
Tool Introduction:Tool Introduction:Weather DataWeather Data
Web Query of Hourly Temperatures Web Query of Hourly Temperatures for Nearest Sitefor Nearest Site
Heating Degree-Hours are Calculated Heating Degree-Hours are Calculated for Each Billing Period,for Each Billing Period,divided by 24, anddivided by 24, anddivided by the number of days in the divided by the number of days in the billing period.billing period.
Tool DemoTool Demo
Forecasting SavingsForecasting SavingsFor Proposed ProjectsFor Proposed Projects
Weather-dependent load is assumed to have the Weather-dependent load is assumed to have the same relationship (slope) as past projects. same relationship (slope) as past projects.
Non-weather-dependent load is assumed to be Non-weather-dependent load is assumed to be proportional to number of scheduled hours.proportional to number of scheduled hours.
UncertaintyUncertainty• 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 UncertaintyStatistics and Uncertainty
International Performance Measurement and International Performance Measurement and Verification Protocol, Volume 1Verification Protocol, Volume 1, 2009., 2009.
ASHRAE Guideline 14-2002, ASHRAE Guideline 14-2002, Measurement of Measurement of Energy and Demand Savings, 2002, Energy and Demand Savings, 2002, Annex BAnnex B..
CCC: CCC: Guidelines for Verifying Existing Building Guidelines for Verifying Existing Building Commissioning Project Savings, Using Interval Commissioning Project Savings, Using Interval Data Energy Models: IPMVP Options B and C, Data Energy Models: IPMVP Options B and C, 2008.2008.
National Institute of Standards and Technology. National Institute of Standards and Technology. The NIST The NIST Engineering Statistics Handbook,Engineering Statistics Handbook,http://www.itl.nist.gov/div898/handbook/index.htmhttp://www.itl.nist.gov/div898/handbook/index.htm
Statistics and UncertaintyStatistics and Uncertainty BPA Regression Reference GuideBPA Regression Reference Guide
(in revision)(in revision) Sections of Particular Relevance:Sections of Particular Relevance:
• Requirements for RegressionRequirements for Regression
• Validating ModelsValidating Models Statistical Tests for the ModelStatistical Tests for the Model Statistical Tests for the Model’s CoefficientsStatistical Tests for the Model’s Coefficients Additional TestsAdditional Tests
Plus, Tables of Statistical MeasuresPlus, Tables of Statistical Measures
Statistics and UncertaintyStatistics and Uncertainty T-statisticT-statistic
• The t-statistic is a measure of the statistical The t-statistic is a measure of the statistical significance of a model’s coefficient. If it is significance of a model’s coefficient. If it is greater than the comparison “critical”greater than the comparison “critical”t-statistic, the coefficient is significant.t-statistic, the coefficient is significant.
• Critical t-statistics are a function of the Critical t-statistics are a function of the required (input) confidence level and the required (input) confidence level and the number of data points. For 24 data points, number of data points. For 24 data points, and a 90% confidence level, the criticaland a 90% confidence level, the criticalt-statistic is 1.72t-statistic is 1.72
Statistics and UncertaintyStatistics and Uncertainty Confidence IntervalsConfidence Intervals
• Confidence intervals are a measure of the Confidence intervals are a measure of the uncertainty of the regression line.uncertainty of the regression line.
• The uncertainty in the savings is dependent The uncertainty in the savings is dependent on the regression uncertainty.on the regression uncertainty.
• The confidence intervals are a function of The confidence intervals are a function of the t-statistic.the t-statistic.
Verified Savings UncertaintyVerified Savings Uncertainty
Meter data measurement uncertainty is Meter data measurement uncertainty is assumed to be zero.assumed to be zero.
Uncertainty of baseline and post regressions Uncertainty of baseline and post regressions are included.are included.
Uncertainty associated with the Uncertainty associated with the appropriateness TMY3 data is not included.appropriateness TMY3 data is not included.
Potential EnhancementsPotential 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 Use Heating Degree-Hours for Forecast Savings as well as
Verified Savings. 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 WthrDataAllow multiple weather sites in WthrData Add capability to benefit from interval meter dataAdd capability to benefit from interval meter data
Comments and QuestionsComments and Questions
Thank YouThank YouBill KoranBill KoranQuantum Energy Services & TechnologiesQuantum Energy Services & [email protected]@quest-world.com
Mira VowlesBonneville Power [email protected]@bpa.gov
Statistics and UncertaintyStatistics and Uncertainty
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Ambient Temperature, ºF
Data
Upper Confidence Line, 95% Confidence Level
Lower Confidence Line, 95% Confidence Level
Upper Confidence Line, 80% Confidence Level
Lower Confidence Line, 80% Confidence Level
Upper Prediction Line, 95% Confidence Level
Lower Prediction Line, 95% Confidence Level
Linear (Data)
We are 80% confident that thetrue regression falls between these lines.
We are 95% confident that thetrue regression falls between these lines.
We are 95% confident that an individual point will fall between these lines.
Statistics and UncertaintyStatistics and Uncertainty p-valuep-value
• The p‑value is the probability that a The p‑value is the probability that a coefficient or independent variable is coefficient or independent variable is notnot significantly related to the dependent significantly related to the dependent variable.variable.
• Rather than requiring an input confidence Rather than requiring an input confidence level as for the t‑statistic, the p‑value level as for the t‑statistic, the p‑value provides probability as an output.provides probability as an output.