The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art

Buildings consume a significant amount of energy, estimated at about one-third of total primary energy resources. Building energy efficiency has turned out to be a major issue in limiting the increasing energy demands of the sector. Literature shows that building user behavior can increase the efficiency of the energy used in the building and different strategies have been tested to address and support this issue. These strategies often combine the quantification of energy savings and qualitative interpretation of occupant behavior in order to foster energy efficiency. Strategies that influence building occupant behaviors include eco-feedback, social interaction, and gamification. This review paper presents a study conducted on the state of the art related to the impact of building user behavior on energy efficiency, in order to provide the research community with a better understanding and up-to-date knowledge of energy, comfort-related practices, and potential research opportunities. Achieving and maintaining energy-efficient behavior without decreasing the comfort of building occupants still represents a challenge, despite emerging technologies and strategies as well as general research progress made over the last decade. Conclusions highlight eco-feedback as an effective way to influence behavior, and gamification as a new opportunity to trigger behavioral change. The impact of user behavior is difficult to quantify for methodological reasons. Factors influencing human behavior are numerous and varied. Multi-disciplinary approaches are needed to provide new insights into the inner dynamic nature of occupant’s energy behavior.

[1]  Janani Vasudev,et al.  A Usability Study of a Social Media Prototype for Building Energy Feedback and Operations , 2014 .

[2]  Astrid Roetzel,et al.  A review of occupant control on natural ventilation , 2010 .

[3]  James A. Landay,et al.  The design of eco-feedback technology , 2010, CHI.

[4]  J. Grimshaw,et al.  Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers , 2004 .

[5]  J. Taylor,et al.  The impact of peer network position on electricity consumption in building occupant networks utilizing energy feedback systems , 2012 .

[6]  Maarten W. Bos,et al.  OPOWER: Increasing Energy Efficiency Through Normative Influence (B) , 2012 .

[7]  Helmut Krcmar,et al.  Motivating domestic energy conservation through comparative, community-based feedback in mobile and social media , 2011, C&T.

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

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

[10]  Richard E. Brown,et al.  After-hours power status of office equipment in the USA , 2005 .

[11]  Masanori Shukuya,et al.  Comparative effects of building envelope improvements and occupant behavioural changes on the exergy consumption for heating and cooling , 2010 .

[12]  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 .

[13]  Kecheng Liu,et al.  Multi-Agent Building Control in Shared Environment , 2007, ICEIS.

[14]  Jian Kang,et al.  A stochastic model of integrating occupant behaviour into energy simulation with respect to actual energy consumption in high-rise apartment buildings , 2016 .

[15]  Michael Johnson,et al.  StepGreen.org: Increasing Energy Saving Behaviors via Social Networks , 2010, ICWSM.

[16]  J. Thøgersen,et al.  Feedback on Household Electricity Consumption: Learning and Social Influence Processes , 2011 .

[17]  D. ürge-Vorsatz,et al.  Potentials and costs of carbon dioxide mitigation in the world's buildings , 2008 .

[18]  H. Rijal,et al.  Thermal comfort in offices in India: Behavioral adaptation and the effect of age and gender , 2015 .

[19]  Ian Walker,et al.  A laboratory test of the efficacy of energy display interface design , 2012 .

[20]  Raja R. A. Issa,et al.  From simulation to monitoring: Evaluating the potential of mixed-mode ventilation (MMV) systems for integrating natural ventilation in office buildings through a comprehensive literature review , 2016 .

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

[22]  Andreas Wagner,et al.  Does the occupant behavior match the energy concept of the building? - Analysis of a German naturally ventilated office building , 2015 .

[23]  I. Vassileva,et al.  The impact of consumers’ feedback preferences on domestic electricity consumption , 2012 .

[24]  P. Linares,et al.  Energy Efficiency: Economics and Policy , 2010 .

[25]  William Chung,et al.  Review of building energy-use performance benchmarking methodologies , 2011 .

[26]  Irmeli Mikkonen,et al.  Evaluation of European energy behavioural change programmes , 2012 .

[27]  Hasanuddin Lamit,et al.  User satisfaction adaptive behaviors for assessing energy efficient building indoor cooling and lighting environment , 2014 .

[28]  Ernest Orlando Lawrence,et al.  An Ontology to Represent Energy- related Occupant Behavior in Buildings Part I: Introduction to the DNAs Framework , 2015 .

[29]  Ardalan Khosrowpour,et al.  Segmentation and Classification of Commercial Building Occupants by Energy-Use Efficiency and Predictability , 2015, IEEE Transactions on Smart Grid.

[30]  Eric Paulos,et al.  Some consideration on the (in)effectiveness of residential energy feedback systems , 2010, Conference on Designing Interactive Systems.

[31]  Rita Streblow,et al.  Energy performance gap in refurbished German dwellings: Lesson learned from a field test , 2016 .

[32]  Riccardo Russo,et al.  The question of energy reduction: The problem(s) with feedback , 2015 .

[33]  Gwendolyn Brandon,et al.  REDUCING HOUSEHOLD ENERGY CONSUMPTION: A QUALITATIVE AND QUANTITATIVE FIELD STUDY , 1999 .

[34]  Scott D. Anderson,et al.  Design and Evaluation of a Social Visualization Aimed at Encouraging Sustainable Behavior , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[35]  Baizhan Li,et al.  Occupants’ behavioural adaptation in workplaces with non-central heating and cooling systems , 2012 .

[36]  John E. Taylor,et al.  Occupant workstation level energy-use prediction in commercial buildings: Developing and assessing a new method to enable targeted energy efficiency programs , 2016 .

[37]  David V. Keyson,et al.  Gaming for energy conservation in households , 2010 .

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

[39]  Sanyogita Manu,et al.  Field studies of thermal comfort across multiple climate zones for the subcontinent: India Model for Adaptive Comfort (IMAC) , 2016 .

[40]  John Psarras,et al.  An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector , 2013 .

[41]  Mahmoud Alahmad,et al.  A Comparative Study of Three Feedback Devices for Residential Real-Time Energy Monitoring , 2012, IEEE Transactions on Industrial Electronics.

[42]  Borong Lin,et al.  Residential heating energy consumption modeling through a bottom-up approach for China's Hot Summer–Cold Winter climatic region , 2015 .

[43]  Ian Beausoleil-Morrison,et al.  A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices , 2013 .

[44]  Vivian Loftness,et al.  The Design and Evaluation of Intelligent Energy Dashboard for Sustainability in the Workplace , 2014, HCI.

[45]  Jaewook Lee,et al.  Conflict resolution in multi-agent based Intelligent Environments , 2010 .

[46]  Wei Chen,et al.  Scalable influence maximization for independent cascade model in large-scale social networks , 2012, Data Mining and Knowledge Discovery.

[47]  Mohamed M. Ouf,et al.  Analysis of real-time electricity consumption in Canadian school buildings , 2016 .

[48]  Filipe Quintal,et al.  Understanding the Limitations of Eco-feedback: A One-Year Long-Term Study , 2013, CHI-KDD.

[49]  Christoph F. Reinhart,et al.  Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control , 2006 .

[50]  Michael A. Humphreys,et al.  ADAPTIVE THERMAL COMFORT AND SUSTAINABLE THERMAL STANDARDS FOR BUILDINGS , 2002 .

[51]  Anthony Rowe,et al.  Toward the Design of a Dashboard to Promote Environmentally Sustainable Behavior among Office Workers , 2013, PERSUASIVE.

[52]  Neil Allan,et al.  Low-energy dwellings: the contribution of behaviours to actual performance , 2010 .

[53]  Mo-Yuen Chow,et al.  Application of functional link neural network to HVAC thermal dynamic system identification , 1998, IEEE Trans. Ind. Electron..

[54]  Darren Robinson,et al.  A generalised stochastic model for the simulation of occupant presence , 2008 .

[55]  Ray Yun Persistent workplace plug-load energy savings and awareness through energy dashboards: eco-feedback, control, and automation , 2014, CHI Extended Abstracts.

[56]  Fulvio Corno,et al.  Home energy consumption feedback: A user survey , 2012 .

[57]  Hani Hagras,et al.  An intelligent agent based approach for energy management in commercial buildings , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[58]  Hom B. Rijal,et al.  Adaptive Thermal Comfort in Japanese Houses during the Summer Season: Behavioral Adaptation and the Effect of Humidity , 2015 .

[59]  Michael Johnson,et al.  Leveraging Social Networks To Motivate Individuals to Reduce their Ecological Footprints , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[60]  Osamu Saeki,et al.  Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data , 2006 .

[61]  Mark Martin,et al.  Post-occupancy evaluation: benefits and barriers , 2001 .

[62]  Zhang Guoqiang,et al.  A novel methodology for identifying associations and correlations between household appliance behaviour in residential buildings , 2015 .

[63]  Edmundas Kazimieras Zavadskas,et al.  Importance of occupancy information when simulating energy demand of energy efficient house: A case study , 2015 .

[64]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[65]  Hua Wang,et al.  Multimodality and Interactivity: Connecting Properties of Serious Games with Educational Outcomes , 2009, Cyberpsychology Behav. Soc. Netw..

[66]  T. Konstantinou,et al.  Designing for residents: Building monitoring and co-creation in social housing renovation in the Netherlands , 2017 .

[67]  Tarja Häkkinen,et al.  Comfort assessment in the context of sustainable buildings: Comparison of simplified and detailed human thermal sensation methods , 2014 .

[68]  Geraldine Fitzpatrick,et al.  Technology-Enabled Feedback on Domestic Energy Consumption: Articulating a Set of Design Concerns , 2009, IEEE Pervasive Computing.

[69]  Sarvapali D. Ramchurn,et al.  Agent-based control for decentralised demand side management in the smart grid , 2011, AAMAS.

[70]  John E. Taylor,et al.  Investigating the impact eco-feedback information representation has on building occupant energy consumption behavior and savings , 2013 .

[71]  P. Gurian,et al.  Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices , 2015 .

[72]  I. Ajzen The theory of planned behavior , 1991 .

[73]  K. Steemers,et al.  Time-dependent occupant behaviour models of window control in summer , 2008 .

[74]  Changbum R. Ahn,et al.  Linking Building Energy-Load Variations with Occupants’ Energy-Use Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM) , 2016 .

[75]  Anna Laura Pisello,et al.  How peers’ personal attitudes affect indoor microclimate and energy need in an institutional building: Results from a continuous monitoring campaign in summer and winter conditions , 2016 .

[76]  Chiara Delmastro,et al.  Generalizable occupant-driven optimization model for domestic hot water production in NZEB , 2016 .

[77]  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 .

[78]  James K. Scarborough,et al.  Increasing Energy Efficiency With Entertainment Media , 2015 .

[79]  Siew Eang Lee,et al.  Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings , 2016 .

[80]  Willett Kempton,et al.  Comparison groups on bills : Automated, personalized energy information , 2006 .

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

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

[83]  Miguel Á. Carreira-Perpiñán,et al.  Occupancy Modeling and Prediction for Building Energy Management , 2014, ACM Trans. Sens. Networks.

[84]  Marco Simonetti,et al.  Reducing thermal discomfort and energy consumption of Indian residential buildings: Model validation by in-field measurements and simulation of low-cost interventions , 2016 .

[85]  Angela Lee,et al.  The impact of occupants’ behaviours on building energy analysis: A research review , 2017 .

[86]  Zhaoxia Wang,et al.  An occupant-based energy consumption prediction model for office equipment , 2015 .

[87]  M. Newborough,et al.  Energy-use information transfer for intelligent homes : Enabling energy conservation with central and local displays , 2007 .

[88]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

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

[90]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[91]  C. Vlek,et al.  The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. , 2007 .

[92]  Carol C. Menassa,et al.  Framework for selecting occupancy-focused energy interventions in buildings , 2016 .

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

[94]  Hasanuddin Lamit,et al.  Correlation Study on User Satisfaction from Adaptive Behavior and Energy Consumption in Office Buildings , 2014 .

[95]  Elie Azar,et al.  A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings , 2012 .

[96]  Shishir Bharathi,et al.  Competitive Influence Maximization in Social Networks , 2007, WINE.

[97]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[98]  John E. Taylor,et al.  Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings , 2014 .

[99]  Muhammad Imran,et al.  Individual energy use and feedback in an office setting: A field trial , 2013 .

[100]  F. Siero,et al.  Changing organizational energy consumption behaviour through comparative feedback , 1996 .

[101]  Jessica Granderson,et al.  Building energy information systems: user case studies , 2011 .

[102]  Gail Brager,et al.  Commercial Office Plug Load Energy Consumption Trends and the Role of Occupant Behavior , 2016 .

[103]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

[104]  SangHyun Lee,et al.  An empirically grounded model for simulating normative energy use feedback interventions , 2016 .

[105]  Tiffany Holmes,et al.  Eco-visualization: combining art and technology to reduce energy consumption , 2007, C&C '07.

[106]  Tianzhen Hong,et al.  Ten questions concerning occupant behavior in buildings: The big picture , 2017 .

[107]  Qi Liu,et al.  DEHEMS: creating a digital environment for large-scale energy management at homes , 2013, IEEE Transactions on Consumer Electronics.

[108]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

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

[110]  R. Cole,et al.  Building human agency: a timely manifesto , 2010 .

[111]  Sami Karjalainen,et al.  Should we design buildings that are less sensitive to occupant behaviour? A simulation study of effects of behaviour and design on office energy consumption , 2016 .

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

[113]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[114]  S. Karjalainen Consumer preferences for feedback on household electricity consumption , 2011 .

[115]  Koen Steemers,et al.  Energy retrofit and occupant behaviour in protected housing: A case study of the Brunswick Centre in London , 2014 .

[116]  Tomasz Jaskiewicz,et al.  Co-designing with office workers to reduce energy consumption and improve comfort , 2015 .

[117]  John E. Taylor,et al.  BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy , 2014 .

[118]  G. T. Gardner,et al.  Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions , 2009, Proceedings of the National Academy of Sciences.

[119]  Carlos Henggeler Antunes,et al.  Energy behaviours as promoters of energy efficiency: A 21st century review , 2012 .

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

[121]  A. Bandura Social Cognitive Theory of Mass Communication , 2001 .

[122]  Tianzhen Hong,et al.  Simulation of occupancy in buildings , 2015 .