Linking of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats

[1]  J. Schleich,et al.  Would you prefer to rent rather than own your new heating system? Insights from a discrete choice experiment among owner-occupiers in the UK , 2021 .

[2]  J. Schleich,et al.  The heat is off! The role of technology attributes and individual attitudes in the diffusion of Smart thermostats – findings from a multi-country survey , 2020 .

[3]  I. Monasterolo,et al.  Emergence of New Economics Energy Transition Models: A Review , 2020 .

[4]  A. Flamos,et al.  Understanding technology ownership to reveal adoption trends for energy efficiency measures in the Greek residential sector , 2020 .

[5]  J. Schleich,et al.  A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies , 2019, Energy Economics.

[6]  Xiaohua Sun,et al.  The effects of public subsidies on emerging industry: An agent-based model of the electric vehicle industry , 2019, Technological Forecasting and Social Change.

[7]  Joachim Schleich,et al.  Energy efficient technology adoption in low-income households in the European Union – What is the evidence? , 2019, Energy Policy.

[8]  Emile J.L. Chappin,et al.  Adoption of energy efficient technologies by households – Barriers, policies and agent-based modelling studies , 2019, Renewable and Sustainable Energy Reviews.

[9]  Magnus Moglia,et al.  An Agent-Based Model of Residential Energy Efficiency Adoption , 2018, J. Artif. Soc. Soc. Simul..

[10]  Wander Jager,et al.  Enhancing the Realism of Simulation (EROS): On Implementing and Developing Psychological Theory in Social Simulation , 2017, J. Artif. Soc. Soc. Simul..

[11]  Charlie Wilson,et al.  Benefits and risks of smart home technologies , 2017 .

[12]  Francis G.N. Li,et al.  Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions , 2017 .

[13]  Lorenz M. Hilty,et al.  Enhancing Agent-Based Models with Discrete Choice Experiments , 2016, J. Artif. Soc. Soc. Simul..

[14]  Varun Rai,et al.  Agent-based modelling of consumer energy choices , 2016 .

[15]  Tao Zhang,et al.  Simulating User Learning in Authoritative Technology Adoption: An Agent Based Model for Council-Led Smart Meter Deployment Planning in the UK , 2016, ArXiv.

[16]  M. Noori,et al.  Development of an agent-based model for regional market penetration projections of electric vehicles in the United States , 2016 .

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

[18]  Neil Strachan,et al.  A review of socio-technical energy transition (STET) models , 2015 .

[19]  R. M. Rylatt,et al.  Will domestic consumers take up the renewable heat incentive? An analysis of the barriers to heat pump adoption using agent-based modelling , 2015 .

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

[21]  F. Mattern,et al.  Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches , 2014 .

[22]  Martin Wietschel,et al.  Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation , 2014 .

[23]  Michel L. Ehrenhard,et al.  Market adoption barriers of multi-stakeholder technology: Smart homes for the aging population , 2014 .

[24]  C. Clark,et al.  The Effect of Mail-in Utility Rebates on Willingness-to-Pay for ENERGY STAR®\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{docum , 2014, Environmental and Resource Economics.

[25]  Varun Rai,et al.  Determinants of Spatio-Temporal Patterns of Energy Technology Adoption: An Agent-Based Modeling Approach , 2014 .

[26]  Varun Rai,et al.  Agent-Based Modeling of Energy Technology Adoption: Empirical Integration of Social, Behavioral, Economic, and Environmental Factors , 2014, Environ. Model. Softw..

[27]  Sean Lyons,et al.  Consumer preferences and the influence of networks in electric vehicle diffusion: An agent-based microsimulation in Ireland , 2014 .

[28]  Tobias Schröder,et al.  Changing minds about electric cars: An empirically grounded agent-based modeling approach , 2014, ArXiv.

[29]  Thomas L. Theis,et al.  An agent based approach to the potential for rebound resulting from evolution of residential lighting technologies , 2014, The International Journal of Life Cycle Assessment.

[30]  Romina Martin,et al.  Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol , 2013, Environ. Model. Softw..

[31]  Bertha Maya Sopha,et al.  Adoption and diffusion of heating systems in Norway: Coupling agent-based modeling with empirical research , 2013 .

[32]  Edmund Chattoe-Brown,et al.  Why Sociology Should Use Agent Based Modelling , 2013 .

[33]  Emile J.L. Chappin,et al.  An agent-based model of transitions in consumer lighting: Policy impacts from the E.U. phase-out of incandescents , 2013 .

[34]  Johannes Palmer,et al.  Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation , 2013 .

[35]  M. Raberto,et al.  An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland , 2012 .

[36]  Reinhard Madlener,et al.  Homeowners' preferences for adopting innovative residential heating systems: A discrete choice analysis for Germany , 2012 .

[37]  Zuo-Jun Shen,et al.  Thermostats for the Smart Grid: Models, Benchmarks, and Insights , 2012 .

[38]  Steven N. Durlauf,et al.  Complexity, economics, and public policy , 2012 .

[39]  Sonja Gensler,et al.  A Study of the Diffusion of Alternative Fuel Vehicles: An Agent‐Based Modeling Approach* , 2011 .

[40]  David A. Hensher,et al.  Hypothetical bias, choice experiments and willingness to pay , 2010 .

[41]  Lorraine Whitmarsh,et al.  A transitions model for sustainable mobility , 2009 .

[42]  G. Silverberg,et al.  A percolation model of eco-innovation diffusion: the relationship between diffusion, learning economies and subsidies , 2009 .

[43]  Nina Schwarz,et al.  Agent-based modeling of the diffusion of environmental innovations — An empirical approach , 2009 .

[44]  Gerard P.J. Dijkema,et al.  On the Impact of CO2 Emission-Trading on Power Generation Emissions , 2009 .

[45]  Eric Campo,et al.  A review of smart homes - Present state and future challenges , 2008, Comput. Methods Programs Biomed..

[46]  Erik Jan Hultink,et al.  How Today’s Consumers Perceive Tomorrow’s Smart Products , 2007 .

[47]  Thomas Bruckner,et al.  Public Policy Modeling of Distributed Energy Technologies: Strategies, Attributes, and Challenges , 2005 .

[48]  K. Train,et al.  Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level , 1998, Review of Economics and Statistics.

[49]  Terry J Atherton,et al.  Rebates, Loans, and Customers' Choice of Appliance Efficiency Level: Combining Stated-and Revealed-Preference Data , 1995 .

[50]  D. McFadden.  MEASUREMENT OF URBAN TRAVEL DEMAND , 1974 .

[51]  K. Lancaster.  A New Approach to Consumer Theory , 1966, Journal of Political Economy.

[52]  Thomas Bruckner,et al.  A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda , 2019 .

[53]  É. Chappin,et al.  EMLab-Consumer - Simulating Energy Efficiency Adoption Decisions of European Households , 2019, ESSA.

[54]  Michael Batty,et al.  SERIES Key Challenges in Agent-Based Modelling for GeoSpatial Simulation , 2007 .

[55]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .