Analyzing residential weatherization decisions using hybrid simulation modeling

Weatherizing residential buildings to make them more energy efficient offers great potential for reducing greenhouse gas emissions, conserving resources, and improving occupant comfort and well-being in the face of climate change. However, weatherization adoption rates remain low. Previous research suggests that social norms and energy-related interactions within a community have greater influence on households’ weatherization adoption decisions than financial incentives. Therefore, an increased understanding of how social factors can be leveraged to encourage more households to weatherize is needed. This paper describes a hybrid simulation model on households’ weatherization adoption decisions in an urban community. The simulation model integrates a building energy simulation model with an agent-based model that is embedded in a social network to represent the interactions, adaptations, and decision making of individual households over time. The model is demonstrated for several experimental scenarios for a residential block in the City of Des Moines, Iowa. Results indicate that social connectedness, community leaders, and delivery mechanisms for weatherization assistance all have a significant impact on household weatherization adoption. These results provide insight into the potential of policy interventions to increase weatherization adoption.

[1]  R. Merton.  The Matthew Effect in Science , 1968, Science.

[2]  E. Aronson,et al.  The Effectiveness of Incentives for Residential Energy Conservation , 1986 .

[3]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[4]  S. Wasserman,et al.  Social Network Analysis: List of Illustrations , 1994 .

[5]  Rich Ling,et al.  Measured energy savings from a more informative energy bill , 1995 .

[6]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

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

[8]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[9]  Jerrold W. Grossman,et al.  Famous trails to Paul Erdős , 1999 .

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

[11]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[13]  J. F. Nicol.  Characterising occupant behaviour in buildings : towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans , 2001 .

[14]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[15]  S. Bornholdt,et al.  Scale-free topology of e-mail networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Roger Guimerà,et al.  Robust patterns in food web structure. , 2001, Physical review letters.

[17]  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.

[18]  W. Adger,et al.  Social Capital, Collective Action, and Adaptation to Climate Change , 2003 .

[19]  Charles M. Macal,et al.  Tutorial on agent-based modeling and simulation , 2005 .

[20]  Charles M. Macal,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[21]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[22]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[23]  Richard L. Schmoyer,et al.  National Evaluation of the Weatherization Assistance Program: Preliminary Evaluation Plan for Program Year 2006 , 2007 .

[24]  S. Wilcox,et al.  Users Manual for TMY3 Data Sets (Revised) , 2008 .

[25]  Joseph Andrew Clarke,et al.  Development of adaptive algorithms for the operation of windows, fans, and doors to predict thermal comfort and energy use in Pakistani buildings , 2008 .

[26]  Corinna Fischer.  Feedback on household electricity consumption: a tool for saving energy? , 2008 .

[27]  Sanem Sergici,et al.  The Impact of Informational Feedback on Energy Consumption -- A Survey of the Experimental Evidence , 2009 .

[28]  D. Foley,et al.  The economy needs agent-based modelling , 2009, Nature.

[29]  S. Sawilowsky.  New Effect Size Rules of Thumb , 2009 .

[30]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[31]  S. Sawilowsky.  Very large and huge effect sizes , 2009 .

[32]  K. Steemers,et al.  Household energy consumption: a study of the role of occupants , 2009 .

[33]  Ahmad Faruqui,et al.  The impact of informational feedback on energy consumption d A survey of the experimental evidence , 2010 .

[34]  Biyu J. He,et al.  The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.

[35]  W. Adger,et al.  Social capital, individual responses to heat waves and climate change adaptation: An empirical study of two UK cities , 2010 .

[36]  John Scott,et al.  The SAGE Handbook of Social Network Analysis , 2011 .

[37]  Kenneth Chelst,et al.  Value-Added Decision Making for Managers , 2011 .

[38]  C. Flora,et al.  Building community resilience to climate change : Testing the adaptation coalition framework in Latin America , 2011 .

[39]  Walter Willinger,et al.  Mathematics and the Internet: A Source of Enormous Confusion and Great Potential , 2009, The Best Writing on Mathematics 2010.

[40]  H. Allcott,et al.  Social Norms and Energy Conservation , 2011 .

[41]  Merrian C. Fuller.  Driving Demand for Home Energy Improvements: Motivating residential customers to invest in comprehensive upgrades that eliminate energy waste, avoid high utility bills, and spur the economy , 2011 .

[42]  Jeong Tai Kim,et al.  The energy-saving effects of apartment residents’ awareness and behavior , 2012 .

[43]  Carol C. Menassa,et al.  Effect of social network type on building occupant energy use , 2012, BuildSys@SenSys.

[44]  Roger L. Moore,et al.  Social Capital, Place Meanings, and Perceived Resilience to Climate Change* , 2012 .

[45]  V. Dorer,et al.  Urban Physics: Effect of the micro-climate on comfort, health and energy demand , 2012 .

[46]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[47]  Emilio Ferrara,et al.  Topological Features of Online Social Networks , 2011, ArXiv.

[48]  John E. Taylor,et al.  Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation , 2012 .

[49]  Douglas S. Noonan,et al.  Spatial Effects in Energy-Efficient Residential HVAC Technology Adoption , 2013 .

[50]  Rishee K. Jain,et al.  Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behavior of networked users exposed to normative eco-feedback , 2013 .

[51]  Ali Malkawi,et al.  Simulating multiple occupant behaviors in buildings: An agent-based modeling approach , 2014 .

[52]  Enedir Ghisi,et al.  Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation , 2014 .

[53]  Samer Faraj,et al.  Emergence of Power Laws in Online Communities: The Role of Social Mechanisms and Preferential Attachment , 2014, MIS Q..

[54]  Carol C. Menassa,et al.  Impact of Social Network Type and Structure on Modeling Normative Energy Use Behavior Interventions , 2014, J. Comput. Civ. Eng..

[55]  Brian G. Southwell,et al.  Weatherization behavior and social context: The influences of factual knowledge and social interaction , 2014 .

[56]  Diana Hernández,et al.  Benefit or burden? Perceptions of energy efficiency efforts among low-income housing residents in New York City. , 2015, Energy research & social science.

[57]  T. Theis,et al.  Emergent Effects of Residential Lighting Choices: Prospects for Energy Savings , 2015 .

[58]  Jin Wen,et al.  Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .

[59]  John E. Anderson,et al.  Energy analysis of the built environment—A review and outlook , 2015 .

[60]  Matthew L. Spialek,et al.  The Centrality of Communication and Media in Fostering Community Resilience , 2015 .

[61]  M. Greenstone,et al.  Are the Non-Monetary Costs of Energy Efficiency Investments Large? Understanding Low Take-Up of a Free Energy Efficiency Program , 2015 .

[62]  Ken Caldeira,et al.  Impacts of global warming on residential heating and cooling degree-days in the United States , 2015, Scientific Reports.

[63]  Tao Wang,et al.  An Energy-Aware, Agent-Based Maintenance-Scheduling Framework to Improve Occupant Satisfaction , 2015 .

[64]  C. Reinhart,et al.  Three Methods for Characterizing Building Archetypes in Urban Energy Simulation. A Case Study in Kuwait City , 2015 .

[65]  Christoph F. Reinhart,et al.  Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets , 2016 .

[66]  Michael C. Dorneich,et al.  A hybrid simulation model for urban weatherization programs , 2016, 2016 Winter Simulation Conference (WSC).

[67]  T. Reames,et al.  A community-based approach to low-income residential energy efficiency participation barriers , 2016 .

[68]  Georg Holtz,et al.  Exploring Homeowners' Insulation Activity , 2016, J. Artif. Soc. Soc. Simul..

[69]  Ariel Drehobl,et al.  Lifting the High Energy Burden in America’s Largest Cities: How Energy Efficiency Can Improve LowIncome and Underserved Communities , 2016 .

[70]  Xuan Luo,et al.  An agent-based stochastic Occupancy Simulator , 2018 .

[71]  Clinton J. Andrews,et al.  An agent-based model of building occupant behavior during load shedding , 2017 .

[72]  Caroline C. Krejci,et al.  Weatherization Adoption in A Multilayer Social Network: An Agent-based Approach , 2017 .

[73]  Ulrike Passe,et al.  Simulation-based Sensitivity Analysis of Future Climate Scenario Impact on Residential Weatherization Initiatives in the US Midwest , 2017 .

[74]  M. Chester,et al.  Energy efficiency to reduce residential electricity and natural gas use under climate change , 2017, Nature Communications.

[75]  Ian M. Hoffman.  Gauging the Impact of Various Definitions of Low- and Moderate-Income Communities on Possible Electricity Savings From Weatherization: , 2017 .

[76]  C. Bale,et al.  Scaling up local energy infrastructure; An agent-based model of the emergence of district heating networks , 2017 .

[77]  Caroline C. Krejci,et al.  Energy Use and Weatherization Practices: Applications for Agent-Based Modeling to Support Vulnerable Populations , 2018, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[78]  Tianzhen Hong,et al.  Building simulation: Ten challenges , 2018, Building Simulation.

[79]  Yuan Jin,et al.  Modeling occupancy and behavior for better building design and operation—A critical review , 2018, Building Simulation.