Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation

Abstract The exposure and diffusion of energy consumption information in building occupant peer networks has been shown to influence an individual's energy consumption decisions. In this paper, we develop an agent-based computational model for individual energy consumption behavior based on data collected during an experiment on residential energy use. We simulate the building occupants’ decision making and the information transmission process. By comparing the impact of several parameters in the network level computational model and validating the parameters in a second experimental setting, our research serves to clarify how network relations can be leveraged for modifying energy consumption behavior. Network degree and weight were identified as the major structural parameters that impact building occupants’ conservation decisions, while network size was found to have no significant impact. These findings have important implications for the design and effectiveness of residential energy feedback systems designed to promote energy conservation in residential buildings.

[1]  Noah J. Goldstein,et al.  Normative Social Influence is Underdetected , 2008, Personality & social psychology bulletin.

[2]  Ian Richardson,et al.  A high-resolution domestic building occupancy model for energy demand simulations , 2008 .

[3]  William Chung,et al.  A study of energy efficiency of private office buildings in Hong Kong , 2009 .

[4]  Elie Azar,et al.  Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings , 2012, J. Comput. Civ. Eng..

[5]  P Pieter-Jan Hoes,et al.  User behavior in whole building simulation , 2009 .

[6]  P. Stern.  What psychology knows about energy conservation. , 1992 .

[7]  Vladislav Kantchev Shunturov,et al.  Dormitory residents reduce electricity consumption when exposed to real‐time visual feedback and incentives , 2007 .

[8]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[9]  M. Yor,et al.  Continuous martingales and Brownian motion , 1990 .

[10]  John Scott.  What is social network analysis , 2010 .

[11]  Andreas Pyka,et al.  Simulating Knowledge Dynamics in Innovation Networks , 2014 .

[12]  Noah J. Goldstein,et al.  A Room with a Viewpoint: Using Social Norms to Motivate Environmental Conservation in Hotels , 2008 .

[13]  B. Bollobás.  The evolution of random graphs , 1984 .

[14]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  D. Kolokotsa,et al.  Reinforcement learning for energy conservation and comfort in buildings , 2007 .

[16]  S. Mullainathan,et al.  Behavior and Energy Policy , 2010, Science.

[17]  H. Dowlatabadi,et al.  Models of Decision Making and Residential Energy Use , 2007, Renewable Energy.

[18]  Bernard P. Zeigler,et al.  Theory of modeling and simulation , 1976 .

[19]  Omar Khattab,et al.  Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences , 2003 .

[20]  John M. Darley,et al.  Behavioral approaches to residential energy conservation , 1978 .

[21]  T. Pettigrew,et al.  Energy conservation behavior: The difficult path from information to action. , 1986 .

[22]  Sheldon M. Ross.  Introduction to Probability Models. , 1995 .

[23]  A. McMakin,et al.  Motivating Residents to Conserve Energy without Financial Incentives , 2002 .

[24]  Morten Elle,et al.  Learning from the social construction of environmental indicators: From the retrospective to the pro-active use of SCOT in technology development , 2010 .

[25]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[26]  Robert H. Williams,et al.  Our Energy: Regaining Control , 1983 .

[27]  Patrick Thiran,et al.  Layered complex networks. , 2006, Physical review letters.

[28]  G. Nigel Gilbert,et al.  Agent-Based Models , 2007 .

[29]  S. Milgram,et al.  Note on the drawing power of crowds of different size. , 1969 .

[30]  Benjamin C. M. Fung,et al.  A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .

[31]  M. Macy,et al.  FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling , 2002 .

[32]  L. Festinger.  A Theory of Social Comparison Processes , 1954 .

[33]  Roger W. Taylor,et al.  Solar Photovoltaic Power Systems: An Electric Utility R & D Perspective , 1984, Science.

[34]  M. Olsen,et al.  Consumers' Attitudes Toward Energy Conservation , 1981 .

[35]  L. Sjöberg,et al.  Lifestyles, and Risk Perception Consumer Behavior , 2005 .

[36]  Joshua M. Epstein.  Agent-based computational models and generative social science , 1999 .

[37]  John E. Taylor,et al.  Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data , 2010 .

[38]  Noah J. Goldstein,et al.  Descriptive normative beliefs and conservation behavior: The moderating roles of personal involvement and injunctive normative beliefs , 2009 .

[39]  David A. Deese,et al.  Our Energy; Regaining Control , 1981 .