Social-Psychological Factors of Demand Response Programs & a 2X2 Experimental Design On

Social-Psychological Factors of Demand Response Programs & a 2x2 Experimental Design on Energy Use

Candace Pang1 and Elizabeth Price2 Mentors: Dr. Chien-fei Chen3, Dr. Xiaojing Xu3, Erica Davis3, and Jackson Lanier3 1 Oak Ridge High School 2 Bearden High School 3 The University of Tennessee, Knoxville

Abstract

Social science disciplines are underrepresented even though they are necessary for

advancement in energy sectors. To show the importance of social science, a survey was

conducted to discover the effects of social-psychological factors on the acceptance of Demand

Response (DR) programs. The survey asked questions that involved temperature settings,

demographics, and social-psychological factors, such as comfort need. The sample consisted of

1600 participants from four states: Tennessee, Virginia, Texas, and California. The results

were then examined to help utility companies create more acceptable DR programs. Lastly, an

experimental design was created to reveal the effects of different group influences and levels of

control on preferred temperature settings.

I. INTRODUCTION Overconsumption of energy is a problem utility companies have to deal with daily,

particularly during peak hours. Excess energy usage can cause numerous problems for the power

grid, including blackouts. This is problematic in developed countries, such as the United States, that

are so accustomed to using energy.When the power goes out, people panic because they are so

dependant on energy use. Utility companies are working to help reduce the strain on the grid by

encouraging people to decrease their energy usage, especially during peak hours. They do this by

developing Demand Response (DR) programs that work to lessen energy usage in response to high

demand during peak hours. Small and large commercial and industry sectors have adopted DR

programs that appear to be decreasing their energy usage, however the residential sector has not

benefitted from what these programs have to offer. There are a few improvements that could be

made to these programs to make them more appealing to the residential sector, which would greatly

impact the security of the grid during peak hours, since residents consume over 50% of the energy.

Social science will be imperative in creating DR programs that residents will accept. This particular

type of science is often overlooked by other disciplines due to its lack of hard data, however this is

often a misconception. Using social-psychological factors, utility companies can come up with DR

programs that people feel more comfortable with. Social science should be integrated in every

discipline due to the large impact people’s social-psychological views have on acceptance. An

experimental design was also conducted to demonstrate the effects of social psychological and

behavioral factors by studying group influence and control options.

II. LITERATURE REVIEW 2.1. Importance and Integration of Social Science

Individual behavioral and social-psychological choices impact the future of energy as much as the technology. Still, researchers continue to focus most resources on the technical side. For every

$35 spent on energy supply and infrastructure, just $1 is spent on behavioral and demand-oriented energy research. Clearly, there is an undervaluation on the importance of social studies on energy use (Sovacool, 2014). Though some efforts have already been made to integrate social science, in order to seek a more energy efficient future even more must occur. Interdisciplinary crosswork, cooperation, and comparison are at the forefront of this change. Social aspects should also be more emphasized in the scientific process. Lastly, people must learn to solve issues from a problem- oriented rather than technological view (Sovacool et al, 2014).

2.2. Social Factors Influencing Energy Consumption

So far, seven different factors from a social aspect influencing energy use in buildings have been published. These factors include: climate, building-related characteristics, user-related characteristics, building services and operation, building occupants’ behavior and activities, social and economic factors, and indoor environmental quality (Yu et al, 2011). Drawing on specific social factors, the occupant’s attitude, norms, and perceived behavioral control affect the energy consumption. Attitude is defined by some as the favorability of a behavior. Positive attitudes would indicate a more likely acceptance, whereas negative attitudes would most likely deter the occupant from a certain behavior. For example, attitudes towards energy conservation positively correlate to intentions to reduce energy consumption (Abrahamse and Steg, 2011). Norms also induce influence over energy decisions. Subjective norms are defined as the pressure from others to engage in energy efficient practices. Therefore, the occupant could gain approval or suffer disapproval from colleagues concerning energy use habits. Our experimental design study examines the influence of a

specific reference group, coworkers, on energy behaviors (Ciadini et al, 2005). Perceived behavioral

control is defined as the perception of the ease or difficulty of performing a certain behavior, which

is linked to control beliefs, beliefs about the presence of factors that may facilitate or impede

performance of the behavior. This factor is a direct correlation to behaviors and indirect correlation

to behaviors based on influence. For energy studies, perceived behavioral control has been

recognized with positive conservation intentions (Wang et al, 2011). Environmental concern is

linked to less energy use, however, energy consciousness is likely more related to predict energy

conserving behaviors (Chen and Knight, 2014). Therefore, this study explores the impact of energy

consciousness, as well as other social-psychological factors.

III. METHOD

3.1. Participants

This survey questioned people from four different states, two each of similar climates and

political affiliations. These included Tennessee, Virginia, Texas, and California. With 1600

participants, there was roughly an even number of people from each state, including 414 from TN,

412 from VA, 403 from TX, and 402 from CA. From this sample, 51% were females and 49% were

males (we realize that gender is a spectrum rather than a binary, however, for our data analysis, we

included male and female). The majority were Caucasian (55.8%), then Hispanic/Latino (24.0%),

African American (11.1%), Multi-race (2.4%), and Asian (.5%). The relative majority of

participants have an income of $35,000 to $99,999 (42.1%) with less than $34,999 following

(31.6%), and $100,00+ (25.7%) last. These demographic ratios model census data and can be used to scale the larger U.S. population. Additionally, the average cooling setting of participants at home is

72 degrees fahrenheit , while not at home it is 74 degrees fahrenheit, and the efficiency setting is 76 degrees fahrenheit. The average heating setting at home is 72 degrees fahrenheit, while not at home it is 69 degrees fahrenheit, and the efficiency setting is 68 degrees fahrenheit.

3.2. Factor Analysis

To determine if the social-psychological questions for each variable (i.e. trust, privacy concern, etc.) were related, factor analysis was performed. This helped to confirm that the list of varying questions were related and could accurately classify a participant as trusting, very concerned with privacy, etc. After confirming that each question was related to its topic, the group of questions was combined to create one variable that could be tested using a regression model. This was done by averaging the answers together using the scale the participant had available to answer with, ranging from strongly disagree to strongly agree.

3.3. Regression

The variables obtained from factor analysis were used to perform regression models that could identify which variables have the highest impact on acceptance of DR programs. This is vital because being able to inform utility companies of exactly what kind of person will or will not take advantage of DR programs could help them improve upon the programs to make them more easily accepted.

3.4. Experimental Design

A pre-test for an experiment was conducted using workers in the Min H. Kao building as participants. They were first surveyed about their preferred temperature setting in an office scenario.

Next they were given a flyer with one of four messages concerning group influence and control options. These messages had minor differences to keep the experiment consistent. After they read

their individual message, each participant took a follow-up survey that asked, “What is your name?,

What is your objective?, Do you have control over your temperature settings here?, Would you

adjust your temperature setting in response to your message?, Now what is your preferred

temperature setting?” The results were then analyzed to reveal any patterns.

IV. RESULTS Demographics & Social-Psych Impacts on Energy Behavior

After performing regression with demographics and social-psychological factors as the

independent variable and the participants’ cooling temperature in summer, these were the variables

that showed significance with their cooling temperature. The unstandardized variable (B value) is

represented by the number and the degree of significance is represented by the asterisk (one being

the least, three being the most). Negative B values mean there is a negative slope, positive B values

mean there is a positive slope. The older a participant was, the lower their temperature setting was.

The participants who had teens had a lower temperature setting. The less concerned the participant was about energy, the lower their temperature was. The more bill conscious a person was, the higher their temperature was. The more comfort need a person had, the lower their temperature was.

This graph was created to show the significance between different demographics and social- psychological factors and acceptance of one of the many DR programs. The higher number of household occupants, more energy conscious, bill conscious, socially aware and trusting the participant was, more likely they were to accept. The older, more comfort need, and more controlling the participant was, the less likely they were to accept. This graph was created to show the significance between different demographics/social- psychological factors and acceptance of one of the many DR programs and to show how the results differed from the previous graph. The older, higher number of household occupants, more energy conscious, bill conscious, socially aware, perceived behaviorally controlling and trusting the participant was, more likely they were to accept. The more comfort need the participant had, the less likely they were to accept.

Demographics & Social-Psych Impacts on Energy Behavior

This chart represents results from the experimental design pretest. The results showed that the participants who received the energy saving message (either the control or no control) were more likely to raise their preferred temperature setting. Analyzation of the pre-test results revealed that the group influence with the greatest impact on the participants’ preferred temperature setting was the energy saving category.

Energy Saving ✕ Control: Increased their preferred temperature setting.

Energy Saving ✕ No Control: Increase their temperature setting despite their lack of control. Comfort ✕ Control: Generally decreased their temperature setting.

Comfort ✕ No Control: Less likely to increase their temperature settings. 7 out of 12 participants increased their temperature settings after reading these messages, however

all participants adjusted their temperature closer to the optimal relative comfort vs. energy saving

setting.

V. DISCUSSION

5.1. Graph #1

In analysis between several independent variables and the prediction of energy consumption

by cooling temperature, the most stark correlation based on the B value is with energy concern.

Examining the regression between all variables, energy concern can relate to energy consumption.

The regression depicts a linear graph, so the more energy concern can conclude higher cooling

temperatures which equate to less energy use. In this graph, the deduction that more teens signify a

lower cooling temperature, and hence more energy consumption can be made. The B value is

negative, which displays a linear, but negatively-sloped line graph. The value is also extremely

significant, as represented by three asterisks. Like teens, the value for comfort need is also negative.

Naturally, the higher the need for comfort, less need is given for energy concern.

5.2. Graph #2

This time logistic regression was conducted and the dependent variable is the acceptance of

an automatic thermostat device to reduce energy consumption. The variable with the highest

correlation and significance is social norms. Social norms play a large part in the influence of

acceptance of demand response program. This correlation shows that participants are far more likely

to adopt a certain energy behavior as long as others have been doing the same. Bill consciousness is

also highly related and very significant. Those more aware of their monthly utility bills tend to seek

energy saving programs, such as this DR program, to lower the cost of their electricity bills. The number in home is also correlative and significant. Typically, the more residents in a home, the higher the electricity bill will be. Therefore, an acceptance of an energy-saving and economically friendly option is ideal for these situations. However, need for control is represented by a negative B value, just like comfort need in the above graph. Due to the fact that this device will be automatic, occupants will lose control over their thermostat settings. This reality is undesired, especially for those who emphasize a need for control.

5.3. Graph #3

For the last regression, the dependent variable is the acceptance of reducing energy consumption by encouragement. Again, number in home, bill consciousness, energy concern, and social norms positively correlate to acceptance. Here, comfort need is even more negatively related and significant to the dependent. Because this DR program is voluntary, participants would need to manually adjust their temperature setting, an especially resented action for those who request more comfort. The results also show that trust was not a significant factor when participants were deciding to accept this DR program. This contrasts with the previous graph, which shows trust as significant.

This can be explained by examining the differences in the two DR programs. The second is an automatic device that would be installed and adjusted by the utility company. This requires a certain amount of trust from the participant. The third, on the other hand, is a manual program that requires no trust because the participant changes the temperature based on encouragement from their utility company.

The results for this chart expose the most significant message, energy saving. The comfort message was a close second, however, different comfort levels and original temperature setting preferences must be taken into account. The results are most likely biased towards energy saving due to this fact

but the results worked for the purposes of the pretest. The experiment must be conducted in an actual

office setting prior to assumptions about the meaning of the pretest results.

VI. CONCLUSION

6.1 Acceptance of Demand Response Programs

Through analyzation of the survey using regression, demographic variables and social-

psychological factors can be identified that are significant when determining a participant’s

acceptance of DR programs. The older the participant was, the less likely he/she was to accept DR

programs. The greater number of people in their household, the more likely they were to accept the

DR programs. The more energy concern, bill consciousness, trust, social norms, perceived

behavioral control, less comfort need and need for control a participant had, the more likely he/she

was to accept DR programs. The results revealed that there are far more social-psychological factors

than demographic variables that influence acceptance of DR programs. These social-psychological

factors are integral for utility companies to introduce more acceptable DR programs. In order to

determine which social-psychological factors influence a person’s preferences, social science needs

to be introduced into other aspects of science. Social science could then be used to determine the

acceptance of other products and help engineers develop more energy efficient technologies that will

be useful for different types of people.

The goal of the experimental design was to test how different group influences affected

an office worker’s energy usage. The results revealed that the energy saving group influence had

the greatest effect on energy usage. VII. ACKNOWLEDGEMENTS

This work was supported in part by the Engineering Research Center

Program of the National Science Foundation and the Department of Energy

under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program.

We would like to thank our faculty advisor, Dr. Chien-fei Chen, and our mentors, Dr. Xiaojing Xu,

Erica Davis, and Jackson Lanier at the University of Tennessee for their guidance and patience. We

would also like to thank Erin Wills and Morgan Briggs for their help throughout the program.

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