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Rain or shine: Behavioral Efficiency with a 100% chance of savings
Lauren MacMillan, OpowerKerry Kaseman, Otter Tail Power Company
Agenda
1. Behavioral Science & Home Energy Reports2. Measuring & Forecasting Savings3. Otter Tail Case Study4. Other Cool Findings
1. Behavioral Science & Home Energy Reports
Opower today
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The Company• Software as a Service Customer
Engagement Platform
• Serving 90+ utilities in 8 countries• Over 4TWh saved to date
• 40% of US household data under management totaling 300 billion reads
• 500 people in Washington, San Francisco, London, Singapore, Tokyo
Our DNA• Behavioral Science
• Data Science
• Computer Science
Conservation messages printed on door hangers and left on homes
Applied Behavioral Science
Schultz & Cialdini (OPOWER Scientists)Hewlett Foundation San Marcos Study
$$$
Turn off AC &Turn on Fan
Environment
Turn off AC &Turn on Fan
Citizenship
Turn off AC &Turn on Fan
Zero Impact on Consumption
Neighbors
Turn off AC &Turn on Fan
6% Drop inConsumption
People care about what other people are doing
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Energy Efficiency TipsNormative Comparison
Home Energy ReportApplied behavioral science, delivered
Historical ComparisonNeighbor Rank
1. Behavioral Science & Home Energy Reports2. Measuring & Forecasting Savings3. Otter Tail Case Study4. Other Cool Findings
Agenda
Randomized Control Trials Allow Opower To Measure Savings
Random Allocation
Control Group
Test Group
Statistically equivalent
groups
+
ReceiveReports
No Reports+
Targeted households
in utility footprint
Outcome
Opower Programs Ramp Over Time
Savings ramp over first 1-12 months
Typical Savings Curve Associated with Opower Program
Steady state savings after 12-18
months
Savings impacted by report delivery &
seasonality
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
Savi
ngs
of th
e Te
st G
roup
Rel
ativ
e to
Con
trol
Gro
up
Average Steady State Savings = 1.5 – 2.5%
Months since program start
Results From Hundreds of Programs Have Been Measured & Verified
6 12 18 24 30 36 42
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Opower Has Complied A Large Dataset of Program ResultsCumulative ObservationsProgram Months
2008 2010 2012 2014
>500 program years of results
Process For Predicting Future Savings
Measure results monthly to generate data set
Correlate savings to program characteristics
Fit program characteristics to forecast model
1
2
3
Semi-Annually
Monthly
Just completed our last model update in January
What have we learned about the factors that best predict savings?
There Are Several Factors That Help Us Predict Future Program Savings
• Number of reports• Report cadence
• State’s Regulatory Environment
• Utility type
• Energy Consumption
Customer Utility Program
Predictability of Savings Is Important For Utilities
Installed Measure SavingsCustomers x Deemed Savings
Opower Program SavingsCustomers x Usage x Savings
Rate
Deemed Savings are reliable & predictable
Forecasting Reduces Uncertainty Around Savings Rates
Opt-Out nature means known, large number of customers
Opt-In nature means number of customers is unknown
Savings rates & usage are unknown
Pros:
Cons:
Pros:
Cons:
Agenda
1. Behavioral Science & Home Energy Reports2. Measuring & Forecasting Savings3. Otter Tail Case Study4. Other Cool Findings
Partial Solution: Research measurable energy savings from behavioral change programs.
Otter Tail Power Company & OpowerBackground: The Next Generation Energy Act (NGEA), passed in 2007, prioritized energy efficiency in Minnesota, even creating an incentive structure to encourage utilities to help their customers save energy.
Dilemma: Otter Tail was looking for a cost-effective EE program to add to their portfolio to increase savings and contribute to their 2011-2013 filed goal
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
Savi
ngs
of th
e Te
st G
roup
Rel
ativ
e to
C
ontr
ol G
roup
Opower Program Achieved 1.5% Savings Over The Last 2 Years
Average Savings of 1.5%
Program Impact: Overall
Minnesota Requires Utilities to Cut Energy Savings by 1/3»Minnesota utilities can only claim 1/3 of the savings
achieved in behavioral programs towards goals
»Regulators and environmental groups concerned about sustainability of savings
»Ruling is impacting programs and goals
Past Performance Used To Estimate Savings filed for 2014-2016
Otter Tail filed Opower savings goal of ~1,600
MWh per year from 2014-2016, ~5% of filed total kwh goal
Program Impact:MWh Savings By Year, Actual & Forecasted
Million Dollar Questions
»How do utilities forecast long-term savings from behavioral change?
»How should utilities forecast long-term savings from behavioral change?
Agenda
1. Behavioral Science & Home Energy Reports2. Measuring & Forecasting Savings3. Otter Tail Case Study4. Other Cool Findings
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
12:00 a.m. 4:00 a.m. 8:00 a.m. 12:00 p.m. 4:00 p.m. 8:00 p.m.
Peak Time Savings
Peak Increase
Savings Relative To Average Program SavingsIndexed, Average = 100%
How do savings change by time of day or month of year?
0%
50%
100%
150%
200%
250%
12:00 a.m. 4:00 a.m. 8:00 a.m. 12:00 p.m. 4:00 p.m. 8:00 p.m.
Peak Savings are Consistent Across Utilities
Average savings curve
A reliable source that may be included in cost effectiveness calcs as avoided capacity
The same shape is seen across savings curves in many groups & climate typesSavings Relative To Average Program SavingsIndexed, Average = 100%
Thank You
Kerry KasemanSenior Resource [email protected]
Lauren MacMillanAnalytics [email protected]
