Neighbourhood Wattch: Using Speculative Design to Explore Values Around Curtailment and Consent in Household Energy Interactions

Smart energy technologies provide new opportunities for network demand management, while generating data that can provide inference into household activities and which is increasingly valuable for user profiling and targeted marketing. This paper explores the human values implicated by smart energy technologies as energy consumption emerges as a potentially sensitive and attributable data trail. Using a values-based approach involving semi-structured interviews with 39 Australians, we use speculative design to engage participants with the implications of high frequency household energy data and understand users’ values and attitudes towards curtailment, surveillance, accountability and consent. The principal contributions are (1) a nuanced understanding of user values around privacy, responsibility, trust, collectivism and curtailment in relation to interpersonal household energy use data and considerations for speculative designs in this space, and (2) reflections on the design of mechanisms for consumer participation in demand response for energy networks.

[1]  James Pierce,et al.  Design Proposal for a Wireless Derouter: Speculatively Engaging Digitally Disconnected Space , 2016, Conference on Designing Interactive Systems.

[2]  Nuno Jardim Nunes,et al.  Why don't families get along with eco-feedback technologies?: a longitudinal inquiry , 2013, CHItaly '13.

[3]  Susan Wyche,et al.  Values as lived experience: evolving value sensitive design in support of value discovery , 2009, CHI.

[4]  Alan Borning,et al.  A Survey of Value Sensitive Design Methods , 2018, Found. Trends Hum. Comput. Interact..

[5]  Ben Matthews,et al.  Speculative Designs for Emergent Personal Data Trails: Signs, Signals and Signifiers , 2020, CHI Extended Abstracts.

[6]  R. W. Carter,et al.  Socio-economic trends and climate change adaptation: the case of South East Queensland , 2012 .

[7]  Peter Tolmie,et al.  Living with interpersonal data: Observability and accountability in the age of pervasive ICT , 2018, New Media Soc..

[8]  Jack Kelly,et al.  Neural NILM: Deep Neural Networks Applied to Energy Disaggregation , 2015, BuildSys@SenSys.

[9]  J. Stoker,et al.  The Department of Health and Human Services. , 1999, Home healthcare nurse.

[10]  Elke E. Mattheiss,et al.  From Awareness to Empowerment: Using Design Fiction to Explore Paths towards a Sustainable Energy Future , 2015, CSCW.

[11]  H. Patricia McKenna,et al.  Human-Smart Environment Interactions in Smart Cities: Exploring Dimensionalities of Smartness , 2020, Future Internet.

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

[13]  Margot Brereton,et al.  When an eco-feedback system joins the family , 2015, Personal and Ubiquitous Computing.

[14]  Richmond Y. Wong,et al.  Sensor Illumination: Exploring Design Qualities and Ethical Implications of Smart Cameras and Image/Video Analytics , 2020, CHI.

[15]  Phoebe Sengers,et al.  Making by making strange: Defamiliarization and the design of domestic technologies , 2005, TCHI.

[16]  Donald E. Zimmerman,et al.  A group card sorting methodology for developing informational Web sites , 2002, Proceedings. IEEE International Professional Communication Conference.

[17]  Sarvapali D. Ramchurn,et al.  Doing the laundry with agents: a field trial of a future smart energy system in the home , 2014, CHI.

[18]  Per-Anders Hillgren,et al.  Participatory design and "democratizing innovation" , 2010, PDC '10.

[19]  Andrea Forte,et al.  The Politics of Privacy Theories: Moving from Norms to Vulnerabilities , 2020, CHI.

[20]  J. Garduño Espinosa,et al.  [The willingness-to-pay concept in question]. , 2009, Revista de saude publica.

[21]  Andrew Vande Moere,et al.  Street infographics: raising awareness of local issues through a situated urban visualization , 2013, PerDis.

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

[23]  Finn Brunton,et al.  Vernacular resistance to data collection and analysis: A political theory of obfuscation , 2011, First Monday.

[24]  Fábio Borges de Oliveira.  On Privacy-Preserving Protocols for Smart Metering Systems , 2017 .

[25]  Cayla Key,et al.  IoT Data in the Home: Observing Entanglements and Drawing New Encounters , 2020, CHI.

[26]  Hans-Arno Jacobsen,et al.  Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review , 2018 .

[27]  Ian Richardson,et al.  Smart meter data: Balancing consumer privacy concerns with legitimate applications , 2012 .

[28]  Eric C. Larson,et al.  The design and evaluation of prototype eco-feedback displays for fixture-level water usage data , 2012, CHI.

[29]  Paul A. Cairns,et al.  Wattsup?: motivating reductions in domestic energy consumption using social networks , 2010, NordiCHI.

[30]  Aleecia M. McDonald,et al.  The Cost of Reading Privacy Policies , 2009 .

[31]  Hui Jiang,et al.  Energy big data: A survey , 2016, IEEE Access.

[32]  Mark Blythe,et al.  Evaluating Design Fiction: The Right Tool for the Job , 2020, Conference on Designing Interactive Systems.

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

[34]  Matt Malpass.  Speculative Everything: Design, Fiction, and Social Dreaming , 2015 .

[35]  Tom Rodden,et al.  Consent for all: revealing the hidden complexity of terms and conditions , 2013, CHI.

[36]  Lillian J. Ratliff,et al.  Energy Disaggregation and the Utility-Privacy Tradeoff , 2018 .

[37]  Martin Flintham,et al.  Domesticating data: socio-legal perspectives on smart homes and good data design , 2019 .

[38]  Sarah Royston,et al.  Smart energy technologies in everyday life: smart Utopia? , 2014, Technol. Anal. Strateg. Manag..

[39]  George Huitema,et al.  The Neglected Consumer: The Case of the Smart Meter Rollout in the Netherlands , 2011 .

[40]  Susan Leigh Star,et al.  Institutional Ecology, `Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39 , 1989 .

[41]  Yolande Strengers,et al.  Materialising energy and water resources in everyday practices: Insights for securing supply systems , 2012 .

[42]  Irina Shklovski,et al.  Leakiness and creepiness in app space: perceptions of privacy and mobile app use , 2014, CHI.

[43]  Andy Crabtree,et al.  Human Data Interaction: Historical Lessons from Social Studies and CSCW , 2015, ECSCW.

[44]  J. Fischer,et al.  The Connected Shower: Studying Intimate Data in Everyday Life , 2018 .

[45]  Bernardete Ribeiro,et al.  Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems , 2012, Neurocomputing.

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

[47]  Ben Matthews,et al.  Trust Me: Doubts and Concerns Living with the Internet of Things , 2016, Conference on Designing Interactive Systems.

[48]  Zoe Sofoulis.  Big Water, Everyday Water: A Sociotechnical Perspective , 2005 .

[49]  Ben Matthews,et al.  Disconnecting: Towards a Semiotic Framework for Personal Data Trails , 2020, Conference on Designing Interactive Systems.

[50]  Ruzanna Chitchyan,et al.  Peer-to-Peer Energy Markets: Understanding the Values of Collective and Community Trading , 2020, CHI.

[51]  Michael Nye,et al.  Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term , 2013 .

[52]  Chris Speed,et al.  Autonomous Distributed Energy Systems: Problematising the Invisible through Design, Drama and Deliberation , 2019, CHI.

[53]  Martin Tomitsch,et al.  Comparative Feedback in the Street: Exposing Residential Energy Consumption on House Façades , 2011, INTERACT.

[54]  James Pierce,et al.  Smart Home Security Cameras and Shifting Lines of Creepiness: A Design-Led Inquiry , 2019, CHI.

[55]  Sarah Pink,et al.  Smart energy futures and social practice imaginaries: Forecasting scenarios for pet care in Australian homes , 2019, Energy Research & Social Science.

[56]  Panagiotis Petridis,et al.  Sensors, sense-making and sensitivities: UK household experiences with a feedback display on energy consumption and indoor environmental conditions , 2019, Energy Research & Social Science.

[57]  Ben Swift,et al.  Camera Adversaria , 2020, CHI.

[58]  Alan Finkel,et al.  Independent review into the future security of the National Electricity Market: blueprint for the future , 2017 .

[59]  Jonathan Dodge,et al.  Studying always-on electricity feedback in the home , 2010, CHI.

[60]  Andy Boucher,et al.  Power to the people: dynamic energy management through communal cooperation , 2012, DIS '12.

[61]  P. Wesley Schultz,et al.  Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms , 2015 .

[62]  K. Armel,et al.  Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .