Previously at the OERC Symposium 2007
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description
Previously at the OERC Symposium 2007. Encouraging Electricity Savings in a University Hall of Residence Through a Combination of Feedback, Visual Prompts, and Incentives. Presented by: Marthinus Bekker Contributors: Tania Cumming, Dr Louis Leland Jr, - PowerPoint PPT Presentation
Transcript of Previously at the OERC Symposium 2007
Encouraging Electricity Savings in a University Hall of Residence Through a Combination of Feedback, Visual Prompts, and Incentives.
Presented by: Marthinus BekkerContributors: Tania Cumming, Dr Louis Leland Jr,
Julia McClean, Niki Osborne, & Angie Bruining
Department of
Psychology
University of Otago
How we went about it
• Control study with a preceding baseline
• Control: Cumberland College• Intervention: Salmond College
Electricity Recordings stop at
both Colleges
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Finish
Baseline data recording starts at
both Colleges
Intervention starts at Salmond College, normal
readings continue at Cumberland College
The Settings
ControlCumberland College
• 326 residents • Mostly 1st year University
students • Aged 18 to 20 years old• Gender ratio of 59%
female and 41% male• Cumberland College is
located in Central Dunedin in an old nurses residence
• A steam plant situated further along the street drives the majority of heating, and hot water
InterventionSalmond College
• 190 residents • Mostly of 1st Year
University students • Aged 18 years or older• Gender ratio of 63%
female and 37% male• Salmond College is
located in North Dunedin, in a purpose built building.
• An on-site steam plant generates heating and hot water
The Results
College Mean %Electricity saved
Day
Control 5.90%
Intervention 16.19 %
Night
Control 6.44 %
Intervention 10.61 %
Substantial differences between control and Substantial differences between control and intervention savingsintervention savings
Please note differences from abstract due to revision of data Please note differences from abstract due to revision of data sincesince
Summary
• Use of Feedback, Incentives & Visual prompts
• Significant difference in savings• Opportunity for substantial
savings across many colleges• Significant savings can be
achieved with little effort & investment
Recommendations
• Full year study, with first semester as baseline
• Long term follow-up to assess spill over • Daily readings (or could do weekly) • Either:
- Use control collegeOR
- Regression equation that predicts expected usage from baseline period and temperature, humidity, population, etc..
Predicting electricity usage in University Colleges of
Residence By: Marthinus Bekker & Dr Louis Leland Jr,
Control
• 326 residents • Mostly 1st year University
students • Aged 18 to 20 years old• Gender ratio of 59%
female and 41% male• Cumberland College is
located in Central Dunedin in an old nurses residence
• A steam plant situated further along the street drives the majority of heating, and hot water
Intervention
• 190 residents • Mostly of 1st Year
University students • Aged 18 years or older• Gender ratio of 63%
female and 37% male• Salmond College is
located in North Dunedin, in a purpose built building.
• An on-site steam plant generates heating and hot water
Phantom Control
TemperatureHumidity
Previous years usageDay of the Week
Light, UVA, UVBRainETC….
The Idea
How we are doing it
1. Obtain archival electricity with the help of the University’s Energy Manager
2. Obtain archival weather data through the physics department weather station and NIWA
3. Obtain other variables such as semester times
4. Go mining with the various variables using multiple regression analysis
Multiple regression
analysisIn multiple linear regression, the relationship between several independent variables and a dependent variable is modeled by a least squares function, called the linear regression equation.
This function is a linear combination of the various model parameters, called regression coefficients.
A linear regression equation with one independent variable would represent a straight line.
The results are then statistically analysed for significance and predictive value.
Different versions of these equations can then be compared to find the best one.
Wind speed
Wind direction
Rain
Multiple regression
analysisTemperature Humidity Previous years usage
Day of the Week Global radiance
RainDay of the Year Period of the Day Year
UVAUVB
Wind direction
Wind speed Pressure
Equation
Multiple regression
analysisEquatio
n
Electricity usage = Last years electricity usage, Hour of day (Dummy), Year, Day of the year, Day x Year2, Temperature, UVA,
UVB, Day of the week (Dummy), Global radiance
The above variables strongly predicts electricity usage, R2Adjusted (variance explained)=0.929(19,2632), p
<0.000
Standard Error=26.880
Graph
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17/02/2005 5/09/2005 24/03/2006 10/10/2006 28/04/2007 14/11/2007 1/06/2008 18/12/2008
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Questions, comments & Suggestions
Thank You• OERC for funding this summer
bursary project• Hans Pietsch for the electricity data• Brian Niven for help with the non
linear transformations• Dr Louis Leland for his guidance
and support• Psychology department for the
facilities to do all this
