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

Authors:
Joel E. Fischer;Sarvapali D. Ramchurn;Michael Osborne;Oliver Parson;Trung Dong Huynh;Muddasser Alam;Nadia Pantidi;Stuart Moran;Khaled Bachour;Steve Reece;Enrico Costanza;Tom Rodden;Nicholas R. Jennings
Affiliations:
The University of Nottingham, Nottingham, UK;University of Southampton, Southampton, UK;University of Oxford, Oxford, UK;University of Southampton, Southampton, UK;University of Southampton, Southampton, UK;University of Southampton, Southampton, UK;The University of Nottingham, Nottingham, UK;The University of Nottingham, Nottingham, UK;The University of Nottingham, Nottingham, UK;University of Oxford, Oxford, UK;University of Southampton, Southampton, UK;The University of Nottingham, Nottingham, UK;University of Southampton, Southampton, UK
Venue:
Proceedings of the 2013 international conference on Intelligent user interfaces
Year:
2013

Citing 18
Cited 0

Trust in recommender systems

Proceedings of the 10th international conference on Intelligent user interfaces
Intelligibility and accountability: human considerations in context-aware systems

Human-Computer Interaction
Collaborative Recommender Systems for Building Automation

HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
It's not all about "Green": energy use in low-income communities

Proceedings of the 11th international conference on Ubiquitous computing
One size does not fit all: applying the transtheoretical model to energy feedback technology design

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The design of eco-feedback technology

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Agents for energy efficiency in ubiquitous environments

Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Designing eco-feedback systems for everyday life

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Agent-based homeostatic control for green energy in the smart grid

ACM Transactions on Intelligent Systems and Technology (TIST)
Automating energy management in green homes

Proceedings of the 2nd ACM SIGCOMM workshop on Home networks
PreHeat: controlling home heating using occupancy prediction

Proceedings of the 13th international conference on Ubiquitous computing
A user-centric evaluation framework for recommender systems

Proceedings of the fifth ACM conference on Recommender systems
Privacy-preserving smart metering

Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
A Context-Aware Recommendation System to Behavioral Based Authentication in Mobile and Pervasive Environments

EUC '11 Proceedings of the 2011 IFIP 9th International Conference on Embedded and Ubiquitous Computing
Are explanations always important?: a study of deployed, low-cost intelligent interactive systems

Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Beyond energy monitors: interaction, energy, and emerging energy systems

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using mobile phones to support sustainability: a field study of residential electricity consumption

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sustainably unpersuaded: how persuasion narrows our vision of sustainability

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.