Recommending energy tariffs and load shifting based on smart household usage profiling

We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management. Copyright © 2013 ACM

An exploratory study to design constrained engagement in smart heating systems

Smart heating systems that leverage complex models of user preferences and energy consumption within the home and the wider network in order to make intelligent heating decisions have started to be adopted in homes. While heating systems that allow the user to directly manipulate the heating schedule and temperature have been investigated in some detail, little is known about how to strike a balance between encouraging users to interact with the system but not to demand too much of their attention, what research has termed "constrained engagement" with calm technology. In this exploratory study, we investigated how participants responded to a number of scenarios involving a novel smart heating system in order to support controllability, intelligibility and user experience as part of a constrained engagement approach. We focused in particular on when participants wanted to engage with the smart heating system and how explanations from the system could influence user engagement. Our study contributes a better understanding of users' expectations towards smart heating systems that can form the basis of improved user interfaces