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Forecasting and Verifying the
Energy Savings for
Web-Enabled Thermostats
in Portable Classrooms:
A Spreadsheet M&V Tool
Developed for BPA
William E. Koran, P.E.
Quantum Energy Services and Technologies
Mira Vowles, P.E.
Bonneville Power Administration
Contents

Need for this tool

IPMVP Option C

Tool Introduction and Demo

Forecasting

Statistics and Uncertainty

Potential Enhancements

Comments, questions, additional ideas
for enhancements
Need for this Tool
Measurement and Verification
Definition

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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.
The intent of this tool is to provide true M&V.
Visualization of Savings
Chart is similar to IPMVP Figure 1,
Example Energy History
700
Actual Baseline Data
Baseline
Actual Post Data
600
Post
Modeled Baseline
500
400
300
200
100
8/31/2010
7/1/2010
5/1/2010
3/1/2010
12/30/2009
10/31/2009
8/31/2009
7/1/2009
5/1/2009
3/1/2009
12/30/2008
10/30/2008
8/31/2008
7/1/2008
5/1/2008
3/1/2008
12/31/2007
10/31/2007
0
9/1/2007
Average kWh per day during billing period
Electricity Use History and Adjusted Baseline
IPMVP Savings Reporting Options

Reporting Period Basis (“Avoided Energy Use”)
• Baseline is Projected to Reporting Period Conditions
• Avoided Energy Use = Projected Baseline Energy Use
minus Actual Reporting Period Energy Use

Fixed Conditions Basis (“Normalized Savings”)
• Baseline and Post period energy use are Projected to a
set of fixed conditions
• Normalized 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.
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 often made using
regression analysis
Approach Taken by this Tool


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This Tool Uses a Fixed Conditions Basis.
The Energy Use is projected for a typical
year, using TMY3 weather data.
Routine adjustments are made using
regression analysis
Tool Introduction: Worksheets

Instructions

User Interaction
• BillingData
• WthrQuery
• WthrData

Outputs
• ForecastSavings
• VerifiedSavings

Background
Calculations
• PastProjectsData
• Calcs
• RegressionBase
• RegressionPost
Tool Introduction:
Calculation Approach


Based on ASHRAE Guideline 14-2002
Measurement of Energy & Demand Savings,
Annex D, Regression Techniques
Independent Variable
• Average Heating Degree-Hours per Day during
billing period (base 65 ºF)

Dependent Variable
• Average kWh per Day during billing period
Tool Introduction:
Weather Data


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.
Tool Demo
Forecasting Savings
For Proposed Projects


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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.
Uncertainty
• uncertainty in the baseline regression
• uncertainty in the post regression from past projects
• uncertainty due to variation in the past projects.
Statistics and Uncertainty




International Performance Measurement and
Verification Protocol, Volume 1, 2009.
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.
National Institute of Standards and Technology.
The NIST Engineering Statistics Handbook,
http://www.itl.nist.gov/div898/handbook/index.htm
Statistics and Uncertainty


BPA Regression Reference Guide
(in revision)
Sections of Particular Relevance:
• Requirements for Regression
• Validating Models



Statistical Tests for the Model
Statistical Tests for the Model’s Coefficients
Additional Tests
Plus, Tables of Statistical Measures
Statistics and Uncertainty

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.
• 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 1.72
Statistics and Uncertainty

Confidence Intervals
• Confidence intervals are a measure of the
uncertainty of the regression line.
• The uncertainty in the savings is dependent
on the regression uncertainty.
• The confidence intervals are a function of
the t-statistic.
Verified Savings Uncertainty
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
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Meter data measurement uncertainty is
assumed to be zero.
Uncertainty of baseline and post regressions
are included.
Uncertainty associated with the
appropriateness TMY3 data is not included.
Potential Enhancements

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Use a weighted regression.
Adjust the regression for summer occupancy.
Limit baseline to whole years.
Input project start and end dates (use 2 dates).
Use Heating Degree-Hours for Forecast Savings as well as
Verified Savings.
Use variable-base heating degree-hours.
Adjust heating degree-hours for the occupancy schedule.
Incorporate more completed projects in the forecasting.
Protect cell formatting.
Allow multiple weather sites in WthrData
Add capability to benefit from interval meter data
Comments and Questions
Thank You
Bill Koran
Quantum Energy Services & Technologies
503-557-7828
[email protected]
Mira Vowles
Bonneville Power Administration
503-230-4796
[email protected]
Statistics and Uncertainty
Normalized Demand, Watts per Square Foot
8
We are 80%
confident that the
true regression falls
between these lines.
7
6
We are 95% confident
that an individual
point will fall between
these lines.
5
4
3
Data
Upper Confidence Line, 95% Confidence Level
Lower Confidence Line, 95% Confidence Level
2
We are 95%
confident that the
true regression falls
between these lines.
1
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)
0
20
30
40
50
60
70
Ambient Temperature, ºF
80
90
100
Statistics and Uncertainty

p-value
• 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.