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Social Psychological Approaches to
Analyze Demand Response and
Promote Energy Efficiency
Chien-fei Chen
Research Professor &
Director of Education and Diversity
NSF-DOE Engineering Research Center, CURENT
University of Tennessee, Knoxville
Overview of Research Projects
Message Framing, Individual Difference and Intervention
Public Acceptance of Smart Meters, EVs, SHWs and Policy
Demand Response, Customer Segmentation and
Social-psychological Factors
2
Social-Technology: Occupant Behavioral Modeling,
Building Technology, Norms and Networks
Social Factors & Energy Efficiency Behaviors in
Public Buildings
Today’s Topic
Message Framing
Public Acceptance
Demand Response
• Feedback and message faming affects energy
attitude & behavior -> Understanding of
individual differences and decision-making
3
• Public acceptance of smart meter, solar hot
water heater, EV and renewable energy policy ->
Identify social factors and barriers
• Costumer segmentation and social-psych
factors -> Deeper understanding individuals’
decision-making process
Social-Technology
• Occupant behavioral modeling, social-psych
factors, social rewards and network influence
-> broader energy efficiency behaviors through
network influence
Behavioral PatternsHeating/cooling, lighting, electronic devices
usage and other curtailment habits, etc.
Awareness &
Knowledge
Appliances & devices (items, types and
ages), awareness of energy efficiency
technologies and energy assistance
programs, etc.
Social-psychological
Factors
Energy saving attitudes, behavioral control,
energy concern, social norms, sense of
community, thermal comfort, money
consciousness, trust, privacy concern,
willingness to adopt new technologies, etc.
DemographicsGender, income, race, education, political
orientation, household size, number of
children and seniors, household dynamics,
areas, etc.
Background: Individual-Level Factors
4
Category Factors/Variables
Strategies to Change Behaviors & Accept DR
• Feedback programs (direct vs. indirect)
• Social norms: descriptive and injunctive norms shape
people’s behaviors; developing strategies in a social
context
• Goal setting: define what people are trying to attain and
be able to evaluate their progress
• Message framing: emphasizing a particular aspect of an
object/activity while limiting emphasis on other aspects
• Commitments: help people to sure their actions are
consistent with their ideals
5
Average Household Electricity Savings of
Historical Program by Feedback Type
Potential Resource
Savings:
20-35%
Real-Time
Plus
Feedback w/Smart
Program
Plus Smart Application of S.S. Insights
6
Social Norms Approach: Opower
7
Message Framing and Electricity Saving
• RQ1: Which type of messages can 1) change attitudes
toward electricity saving? 2) boost perceived efficacy
(whether can I make a difference) on saving electricity?
• RQ2: How individual differences react to messages
differently?
8
Benefit framing:
Environmental vs. Money
Temporal framing:
Long vs. Short-term
Condition 1, 2: Financial, Long-term/Short-term
9
10
Condition 3, 4: Environmental, Long-term/Short-term
10
Results of Message Framing
• 292 US residents
• Environmental messages, in general, are more
effective than the financial benefits in producing
positive attitudes toward energy saving.
• Short-term benefits boost perceived efficacy
among people with lower environmental concern.
11
X. Xu, L. M. Arpan, and C. F. Chen. The Moderating Role of Individual Differences in
Responses to Benefit and Temporal Framing of Messages Promoting Residential Energy
Saving, Journal of Environmental Psychology, 2015, doi: 10.1016/j.jenvp.2015.09.004.
Theory of the Planned Behavior(Icek Ajzen, 1991)
12
Social-Psych Factors Affecting the Use of Solar
Water Heaters, EVs and Policy Support
* p < 0.05; ** p < 0.01, *** p < 0.00113
Public Acceptance of Smart Meters (Extended Technology Acceptance Theory)
Usefulness privacy
Useful2
Useful1
Useful8
Useful3
Useful4
Useful5
Useful6
Money Money2
Money1
Money3
Money4
Money6
Priv4R Priv6RPriv5R Priv7R
EnvironEnviron2
Environ1
Trust
Trust1 Trust2 Trust3 Trust4
Support
Environ4
Environ3
.89
.89
.87
.80
.88
.84
.76
.61
.56
.83
.83
.70
.84
.87
.73
.95
.74 .92 .91 .94
.95 .89 .83 .92
HabitE1
HabitCH
.80
Habit7
Price
Price2 Price3
.93 .85
HabitC1
Habit6
.68
.51
-.14, p < .001.52, p < .001
-.018
.014
-.059
-.007
.091, p = .01
-.093, p = .001
.035
Sampled 817 U.S. residents across the U.S.
14
Social-Psych Factors, Segmentation and DR
• Traditional approach to promoting demand response
(DR): economic approach – peak & off-peak pricing,
dynamic pricing, additional financial incentives, etc.
• To what extent the financial incentives help
customers to accept DR programs?
• Is the effect same for everyone?
• How to predict acceptance from energy use habits,
demographic variables, and social-psychological
factors?
15
Financial Incentives & HVAC-Related DR
• Goal: more accurate estimation of adjustable loads a function of financial incentives and other individual (social-psych)
household characteristics
• Method: Two online surveys across 48 states in the U.S. 711 and 754 valid responses collected
16
Ex: How much monetary rewards do you expect in exchange for… ?
• Adjusting AC setting by 2-3oF when at home
• Adjusting heater by 2-3oF when at home
• Adjusting AC setting by > 5oF or shutting AC down
before leaving home
• Adjusting heater setting by > 5oF or shutting heater down
before leaving home
Financial Incentives & HVAC-Related DR
17
9.90%9.10%
11.30% 11.70%
9.80%9.00%
7.80% 8.00% 7.10%
6.00%
12.60%13.30%
18.30%
14.10%
10.50% 11.00%
20.70%
17.20%
5.30% 5.80%
10.70%
12.10%
11.00%
12.30%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
Adjust AC for 2-3°F_at home
Adjust heater for 2-3°F_at home
Adjust AC for>5°F_away
Adjust heater for>5°F_away
Automation_AC Automation_Heater
no incentive
<5% of montly bill
5%-10% of monthly bill
would not do it
Ac
ce
pta
nc
e r
ate
s in
re
sp
on
de
nts
Preference on Incentive Type
18
5.82.2 0.2
25.7
65
0.6 0 0.5 Amazon GC
Grocery/Wholesale GC
Dental/Health visit
Cash
Reduction on next month'sbill
Movie GC
Restaurant GC
Donation
Predictors of HAVC-Related DR
Energy Use Info Demographics Social-Psychological
Monthly Bill_Average Age Energy Concern
Stay Home (9am-
5pm)Gender Bill Consciousness
Light Use Income Frugality
Computer Sleep
ModeEducation Need for Comfort
Thermostat Settings Political Orientation Need for Convenience
Night Adjustments House Sqft Need for Control
Household Size Trust
Weather Region Subjective Norm
Perceived ControlBlock 1 Block 2 Block 3
19
20
Step 1: habits
Step 2: +
demographics &
house features
Step 3: + social-
psychological
variables
• Average bills in the
summer
• Use of sleep mode on
computers
• Age
• Need for coolness
Voluntarily Adjusting A/C settings by 2-3oF
• Average bills in the
summer
• Light use habits
• Age
• Income
• Energy concerns
• Frugality
• Trust in the utility
company
• Need for coolness
Letting utility company adjust A/C by 2-3oF
R2 = .04
R2 = .07
R2 = .15
R2 = .04
R2 = .06
R2 = .18
Hierarchical
Regression
results:
most cooperative
29.2%
45.0% 43.6%
75.6%
46.0%
5.4%6.7%
3.0%
54.9%
22.9%
6.9%7.8%
2.6%0.0%
2.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Adjust HC for 2-
3°F_at home
Adjust HC for
>=5°F/turn off_
away
Let utility
company
adjust 2-3°F
Emergency shut
down 10mins
Emergency shut
down 30mins
Cluster 1 (n=291)
Cluster 2 (n=297)
Cluster 3 (n=116)
Ac
ce
pta
nc
e r
ate
s in
clu
ste
rsDifferences in Acceptance Rates
Different clusters respond to no rewards or a reward <5% monthly bill
21
MostCooperative
Cluster
Lower Comfort Need in Summer
Higher Concern for
Environmental Impacts
More Frugal
LowerElectricity
Bills
Smaller Homes
Example of Customer Segmentation
Characteristics of most cooperative group in voluntarily
raising thermostat settings in summer
22
Predicting HAVC-DR (Segmentation)
• Accepting automation at home in summer
23
• Higher thermostat
settings generally
• Lower electricity bills
• More Democrats
• Higher energy concerns
• More frugal
• Lower comfort needs
• Lower needs for
convenience
• Positive energy-saving
attitudes
Features of most
cooperative group
• Lower thermostat
settings generally
• Higher income
• Lower energy concerns
• Lower money
consciousness
• Lower trust
• Poorer energy-saving
attitudes
Features of least
cooperative group
Hierarchical
logistic
regression
results:
Social-Psych & Demographics Factors
Predicting DR Programs
24
Structure equation modeling results:
General Findings
• Finding 1: Social-psychological variables enhanced the
predictive power of the overall model.
• Finding 2: The amount of the requested incentives did
not have a linear relationship with certain factors.
E.g. Small homes -> smaller amount of requested
incentives to lower thermostat setting in winter, large
homes is not associated with higher requested
incentives.
25
26
• How attractive are current DR programs in the market?
Automatic A/C cycling
Automatic A/C shut down
Automatic thermostat adjustment
Voluntary A/C adjustment
• How specific features of DR programs affect attractiveness?
Frequency, duration, etc.
Level of control (e.g., opt out options)
Rewards (one-time vs. consumption-dependent)
• Predictable by energy use habits, demographics and social-
psychological variables?
27
What are the factors affecting your preference of DR programs? Please drag the left factors and drop them into the appropriate blocks on the right.
28
Future Work: Integration of Social-psych
Approaches and Technology
2. Test-Bed Study:
Test social-technological
interventions: Real-time
Prepare for the field
experiment
1. National
Representative Survey: Determine patterns and
barriers
29
3. Field
Experiment:
Find out the
most effective
Intervention
Step 1 (Baseline): Education and kWh Usage
Feedback
Step 2: Expenditure Feedback (self) 1
Step 3: Energy Impact Feedback (self) 21
Step 4: Relative on Expenditure
(self vs. others)21 3
Step 5: Relative on Energy Impacts
(self vs. others)32 41
Step 6: Social Rewards and Network
Influence43 521
Enabling
Technologies
Real-time
Energy
Monitoring
Mobile
Technology
Energy Impact
Estimation-
Based
Modeling
Test-Bed Pilot Study: Four Households
30
Conclusions
• Human beings are not always rational.
• Information programs (e.g., energy saving programs) may be effective in changing attitudes but may not be effective in changing behaviors.
• Financial incentive is not the only factor to adopt DR programs or reduce energy consumption.
• The approaches of social norm, social networks and message framing could be effective.
• Don’t assume people will automatically adopt DR programs; need to consider different type of individuals (customer segmentation) and situations.
• Energy use in public domain relates largely to social matters, not individual ones.
• Integration of social-technological approaches are needed for persistent behavioral change and technology adoption.
31
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