Gamification-based framework for engagement of residential customers in energy applications

Abstract According to the European Union Third Energy Market package, the roll-out of smart meters in the residential sector can presumably play a key role in reaching the goals of sustainability strategies. However, the deployment of smart meters alone does not necessarily drive residential customers to use energy in a more sustainable manner. Therefore, more attention should be paid to customers energy behavior in order to reach the objectives of the roll-out policy. In this study, we propose an interdisciplinary framework that establishes a behavioral model to identify the main energy-related behavior change requirements necessary to engage residential customers in energy applications. To fulfill the requirements, we first present the technical system architecture that enables energy applications for residential customers. Then, we assess how gamification, which is the employment of game design elements in non-game contexts, can be used to enhance energy applications by driving customer engagement and energy-related behavior change. To do that, the most relevant game design elements are discussed and classified. After that, the expected value streams from using a gamification-based solution for different stakeholders in the energy market are identified. Finally, the study discusses the potential of the proposed framework in different energy applications for residential customers.

[1]  John A. Wagner,et al.  Organizational Behavior , 2020 .

[2]  Boris Bellalta,et al.  A distributed power sharing framework among households in microgrids: a repeated game approach , 2016, Computing.

[3]  Haitham Abu-Rub,et al.  Smart grid customers' acceptance and engagement: An overview , 2016 .

[4]  Manel Guerrero Zapata,et al.  Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery , 2017 .

[5]  Deborah I. Fels,et al.  Gamification in theory and action: A survey , 2015, Int. J. Hum. Comput. Stud..

[6]  Alexander L. Davis,et al.  Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters , 2012 .

[7]  Saul Greenberg,et al.  One size does not fit all: applying the transtheoretical model to energy feedback technology design , 2010, CHI.

[8]  W. V. Sark,et al.  Techno-economic analysis of household and community energy storage for residential prosumers with smart appliances , 2018 .

[9]  W. Fichtner,et al.  Smart Homes as a Means to Sustainable Energy Consumption: A Study of Consumer Perceptions , 2012 .

[10]  Yolande Strengers,et al.  Smart Energy Technologies in Everyday Life: Smart Utopia? , 2013 .

[11]  Herre van Oostendorp,et al.  Steps to Design a Household Energy Game , 2015, Int. J. Serious Games.

[12]  Kiran Lakkaraju,et al.  Small Is Big: Interactive Trumps Passive Information in Breaking Information Barriers and Impacting Behavioral Antecedents , 2016, PloS one.

[13]  B. Neenan,et al.  Societal Benefits of Smart Metering Investments , 2008 .

[14]  Daniel Nilsson,et al.  Photovoltaic self-consumption in buildings : A review , 2015 .

[15]  Linda Steg,et al.  What Drives Energy Consumers?: Engaging People in a Sustainable Energy Transition , 2018, IEEE Power and Energy Magazine.

[16]  Lennart E. Nacke,et al.  From game design elements to gamefulness: defining "gamification" , 2011, MindTrek.

[17]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[18]  H. Staats,et al.  Effecting Durable Change , 2004 .

[19]  Yonghong Kuang,et al.  Smart home energy management systems: Concept, configurations, and scheduling strategies , 2016 .

[20]  Fulli Gianluca,et al.  Guidelines for cost benefit analysis of smart metering deployment , 2012 .

[21]  D. Keyson,et al.  Exploring the use of a game to stimulate energy saving in households , 2012 .

[22]  Sean B. Walker,et al.  Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling , 2014 .

[23]  Wokje Abrahamse.  Energy conservation through behavioral change : Examining the effectiveness of a tailor-made approach , 2007 .

[24]  Anton Gustafsson,et al.  Evaluation of a pervasive game for domestic energy engagement among teenagers , 2008, ACE '08.

[25]  Linda Steg,et al.  FACTORS INFLUENCING THE ACCEPTABILITY AND EFFECTIVENESS OF TRANSPORT PRICING. IN: ACCEPTABILITY OF TRANSPORT PRICING STRATEGIES , 2003 .

[26]  Martin Hahmann,et al.  Leveraging gamification in demand dispatch systems , 2012, EDBT-ICDT '12.

[27]  Anton Gustafsson,et al.  Power explorer: a casual game style for encouraging long term behavior change among teenagers , 2009, Advances in Computer Entertainment Technology.

[28]  Riccardo Russo,et al.  The British public’s perception of the UK smart metering initiative: Threats and opportunities , 2016 .

[29]  Anton Gustafsson,et al.  Persuasive design of a mobile energy conservation game with direct feedback and social cues , 2009, DiGRA Conference.

[30]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[31]  Varun Rai,et al.  Play and learn: Serious games in breaking informational barriers in residential solar energy adoption in the United States , 2017 .

[32]  Mladenka Tkalcic,et al.  Transtheoretical model of behavior change , 2011 .

[33]  Michael Nye,et al.  Social experiments in sustainable consumption: an evidence-based approach with potential for engaging low-income communities , 2008 .

[34]  F. Alvarado,et al.  Designing incentive compatible contracts for effective demand management , 2000 .

[35]  Anton Gustafsson,et al.  Promoting New Patterns in Household Energy Consumption with Pervasive Learning Games , 2007, PERSUASIVE.

[36]  Thomas A. Seder,et al.  Driving the Scoreboard: Motivating Eco-Driving Through In-Car Gaming , 2011 .

[37]  Ong Hang See,et al.  A review of residential demand response of smart grid , 2016 .

[38]  H. Oostendorp,et al.  A Method for Developing a Game-Enhanced Tool Targeting Consumer Engagement in Demand Response Mechanisms , 2018, Progress in IS.

[39]  Paulo F. Ribeiro,et al.  History of demand side management and classification of demand response control schemes , 2013, 2013 IEEE Power & Energy Society General Meeting.

[40]  Wenyan Wu,et al.  Predicting household water use behaviour for improved hygiene practices in internet of things environment via dynamic behaviour intervention model , 2016, IET Networks.

[41]  E S Geller,et al.  Reaction to Willems and McIntire’s Review of “Preserving the environment: New strategies for behavior change” , 1984, The Behavior analyst.

[42]  Lea Schick,et al.  Flexible and inflexible energy engagements—A study of the Danish Smart Grid Strategy , 2015 .

[43]  Paul C. Stern,et al.  Environmental Problems and Human Behavior , 1995 .

[44]  Kristiina Hukki,et al.  Smart use of electricity - How to get consumers involved? , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[45]  Bala Venkatesh,et al.  Smart metering and functionalities of smart meters in smart grid - a review , 2015, 2015 IEEE Electrical Power and Energy Conference (EPEC).

[46]  C.W. Gellings,et al.  The concept of demand-side management for electric utilities , 1985, Proceedings of the IEEE.

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

[48]  M. McHenry.  Technical and governance considerations for advanced metering infrastructure/smart meters: Technology, security, uncertainty, costs, benefits, and risks , 2013 .

[49]  Fulli Gianluca,et al.  Smart Grid Projects Outlook 2014 , 2014 .