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

Journal article

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.

Authors: Fischer, J; Ramchurn, S; Osborne, M; Parson, O; Huynh, T

• Journal: International Conference on Intelligent User Interfaces, Proceedings IUI
• Pages: 383-394
• Publication date: 2013-03-19
• DOI: 10.1145/2449396.2449446
• Language: English
• Keywords: Recommender systems; Load shifting; Personalization; Demand response; Energy tariffs; Smart grid