A Field Study of Human-Agent Interaction for Electricity Tariff Switching

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called TariffAgent, to study notions of flexible autonomy in the context of tariff switching. TariffAgent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonomy.

Keywords: Human-Agent Interaction, Autonomous Agents, Flexible Autonomy, Energy, Smart Grid.

Citation: Alper Alan, Enrico Costanza, Joel Fisher, Sarvapali Ramchurn, Tom Rodden and Nicholas Jennings. A Field Study of Human-Agent Interaction for Electricity Tariff Switching.

Download: http://bit.ly/1MBoStF

About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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