Motivation The Problem

Transcription

1 Motivation The Problem 2010 U.S. Energy Consumption Industry Buildings Transport Source: EIA 2010 Take back effect To avoid the most devastating effects of climate change, we require behavior mechanisms to reduce energy consumption in buildings. 1

2 Influencing Energy Consumption Behavior Peer / Social Networks Despite pervasive presence of social networking in our lives, very little scientific research has examined the impact of peer influence on energy use decisionmaking when users are provided with real-time feedback on consumption Yet we are starting now to see some attempts by industry to use networks to elicit energy saving 2

3 Do social / peer networks impact individual energy consumption behavior? Yes, but 3

4 The Experiments Experiment 1: Do social networks impact energy consumption if users are provided feedback? Yes, but: Interface for viewing data was clunky (low engagement) Study was only 2 weeks so not clear what dynamics drove consumption reductions Experiment 2: Representation Does the way in which feedback is presented impact performance of system? Experiment 3 / Simulation 1: Network Dynamics What dynamics of social networks actually impact energy consumption reductions? What kind of representation drives consumption reductions? 4

5 Test Bed Building & Sensor Infrastructure Watt Hall (yes Watt) 6 floors, 89 rooms Individual room metering Occupied by juniors & seniors (~stable peer networks) Do not directly pay for energy Electric loads: computers, televisions, stereos, lighting, refrigerators, electric stoves Data Logger Web Interface Current Transducer Web Server 5

6 Experiment 1: Impact of Networks Study Design Study Details: 2 weeks in duration (per Petersen et al.2007) 1 week of pre-study data collected Electricity consumption profiles sent to participants on Day 1 and after 1 week Peer network surveys completed to assign networks Study Groups: A: View own past and present use (9) B: A+ individual vs. building average energy use (9) C: A+B+ individual vs. peer network average use (19) Control (46) Peschiera, G., Taylor, J., and Siegel, J. (2010). "Response-Relapse Patterns of Building Occupant Electricity Consumption Following Exposure to Personal, Contextualized and Occupant Peer Network Utilization Data," Energy and Buildings, 42(8):

7 Experiment 1: Hypothesis & Results Hypothesis 1: Participants will exhibit greater electricity use reduction than the control group. DP before mean = -14 Comparing 95% confidence interval upper bound lower bound Verifies that feedback does DP to 2weeks reduce mean = -26 consumption * p * = statistically significant (p <.05) Comparing 95% confidence interval upper bound lower bound p Hypothesis 2: If occupants are exposed to personal electricity use data contextualized by the building average (Group B), then they will reduce electricity use more than Group A DA before mean = -14 DB before mean = -16 DA before mean = -15 To DA 1 mean = Inconclusive if providing building DB To average data 1 alone will reduce mean = -22 To DA 2 consumption mean = DB before DB To 2 mean = -38 mean = -62 * = statistically significant (p <.05) * Hypothesis 3: If occupants are exposed to personal electricity use data contextualized by peer network as well as building average (Group C), then they will reduce their electricity usage more than; (i) Group B or (ii) Group A DC before mean = -13 DC 1 95% confidence interval Comparing Users who viewed upper bound socially lower bound contextualized data of peers in To * network mean = reduced -34 the most DC before DC To * mean = -17 mean networks = -45 matter! * = statistically significant (p <.05) p Peschiera, G., Taylor, J., and Siegel, J. (2010). "Response-Relapse Patterns of Building Occupant Electricity Consumption Following Exposure to Personal, Contextualized and Occupant Peer Network Utilization Data," Energy and Buildings, 42(8):

8 Experiment 2: Representation Prototype Eco-Feedback User Interface User Interface Utilization Data Collection Usage data was captured using web-analytics and clickstream technology. Six week empirical study resulted in 1,410 data points being captured from the following activities: Logins Views of incentives page Changes to historical view Addition of peers for social comparison Energy audit submissions Jain, R., and Taylor, J. (under revision). "Assessing Eco-Feedback Interface Usage and Design to Drive Energy Efficiency in Buildings," Energy and Buildings. [Working Paper Version Available Upon Request] 8

9 Experiment 2: Hypothesis & Results Hypothesis 1: Participants who reduced their electricity consumption relative to the Control Group will have visited the prototype interface more often than participants who increased or maintained their electricity consumption relative to the Control Group. Participants Who Reduced Consumption Participants Who Increased Consumption P-value User engagement correlated with Mean reduction 5.13 in energy 2.60 consumption.028* User Logins * p<0.05, ** p<0.01, *** p<0.001 Hypothesis 2: Participants who utilize; (a) Historical comparison, (b) Normative comparison, (c) Incentives or (d) Disaggregation will login more than participants who did not utilize the feature. Mean User Logins by Utilized Component Historical Comparison Normative Comparison Participants Who Used Feature Participants Who Did Not Use Feature P-value (a) Users who used historical (b) comparison and 5.00 incentives 2.40 features.12 had higher user engagement *** (c) Incentives *** (d) Disaggregation * p<0.05, ** p<0.01, *** p<0.001 Hypothesis 3: The sign (positive or negative) of reward points a participant views upon logging in for the first time will correlate with the number of times a participant logs in to the prototype interface. Mean User Logins Participants Who Viewed Positive Points Participants Who Viewed Negative Points Users who viewed negative points P-value at first log-in had lower engagement ** * p<0.05, ** p<0.01, *** p<0.001 Jain, R., and Taylor, J. (under revision). "Assessing Eco-Feedback Interface Usage and Design to Drive Energy Efficiency in Buildings," Energy and Buildings. [Working Paper Version Available Upon Request] 9

10 Experiment 3: Hypothesis & Results Hypothesis 1: If building occupants share electricity utilization data with peers (Study Group B) they will on average reduce their electricity utilization relative to the Control Group more and over a longer time period than if they are only exposed to building per capita electricity utilization (Study Group A). D-value Verifies results of previous experiment over longer study period (feedback reduces consumption) Baseline Study T-test p- Period Period value Study Group A Study Group B * Hypothesis 2: Participants in Study Group B are more likely to show a statistically significant reduction in their electricity utilization relative to the Control Group (a) the higher their network degree and (b) the higher their Eigenvector centrality.. D-value Intercept Coefficient p-value Consumption reduces with network degree and eigenvector centrality dynamics of social networks are drivers of consumption reduction Network Degree *** Eigenvector Centrality * Peschiera, G., and Taylor, J. (under review). "The Impact of Peer Network Position on Electricity Consumption in Building Occupant Networks Utilizing Energy Feedback Systems," Energy and Buildings. [Working Paper Version Available Upon Request] 10

11 Simulation 1: Making Predictions Beyond a Single Building Through simulation we validated the network findings and also examined how changes in the structure of the building occupant network may impact energy conservation. Network degree and weight are structural characteristics that impact conservation decisions not network size We also developed and launched a simplified webbased multi-agent energy and networks simulation where people can examine the impact of changes in network structure on energy consumption. Interested in examining how energy usage changes with network structure? Visit: simulation/energysim_new.html Chen, J., Taylor, J., and Wei, H.H. (under revision). Modeling Building Occupant Energy Consumption Decision-making: The Interplay between Network Structure and Conservation," Energy and Buildings. [Working Paper Version Available Upon Request] 11

12 Conclusion / Implications Conclusions Networks matter in the context of energy conservation More research is required to understand the dynamics of these networks. Simulation of these dynamics will provide significant insight into energy conservation efforts of users Underlying mechanisms of influence and energy saving practice adoption are unknown what is really causing the reductions in energy use? Does the influence of networks hold for water conservation and energy use in the office environment? 12

13 Future Research Residential Energy Use Commercial Energy Use Integrating Energy at the Water-Energy Nexus Network Dynamics Experiments 1 & 3 Simulation 1 Data Collection in Spring 2012 Representation Experiment 2 Practice Diffusion Data Collection in Spring

14 Acknowledgements & Contact Info Please Contact Us We are Seeking Collaborators Rishee Jain IGERT Fellow Columbia University John E. Taylor Associate Professor and Director of the Civil Engineering Network Dynamics Lab Virginia Tech Gabriel Peschiera Alum MS 2011 Columbia University Rimas Gulbinas PhD Candidate Virginia Tech Jiayu Chen PhD Candidate Columbia University Patricia Culligan Professor of Civil Engineering & Engg Mechanics Columbia University 14