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Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling

Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling
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.
978-1-4503-1965-2
383-394
Fischer, Joel E.
34e61650-62c4-4202-95e8-37aaa849299b
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Osborne, Michael A.
a31bb544-076f-4eb7-8dd2-20cd600fbb6f
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Alam, Muddasser
9a8f7f5d-2a65-4933-8903-d48445358eb8
Pantidi, Nadia
50efca22-a46f-4594-8042-3c5434b95b4c
Moran, Stuart
d397b121-73fd-4cb4-83ef-2be93dc823bf
Bachour, Khaled
f67eb509-5174-4e3e-b967-dac8e729676c
Reece, Steven
b79cac5b-bbd2-4038-b47d-3d4c845802aa
Costanza, Enrico
0868f119-c42e-4b5f-905f-fe98c1beeded
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Fischer, Joel E.
34e61650-62c4-4202-95e8-37aaa849299b
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Osborne, Michael A.
a31bb544-076f-4eb7-8dd2-20cd600fbb6f
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Alam, Muddasser
9a8f7f5d-2a65-4933-8903-d48445358eb8
Pantidi, Nadia
50efca22-a46f-4594-8042-3c5434b95b4c
Moran, Stuart
d397b121-73fd-4cb4-83ef-2be93dc823bf
Bachour, Khaled
f67eb509-5174-4e3e-b967-dac8e729676c
Reece, Steven
b79cac5b-bbd2-4038-b47d-3d4c845802aa
Costanza, Enrico
0868f119-c42e-4b5f-905f-fe98c1beeded
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Fischer, Joel E., Ramchurn, Sarvapali D., Osborne, Michael A., Parson, Oliver, Huynh, Trung Dong, Alam, Muddasser, Pantidi, Nadia, Moran, Stuart, Bachour, Khaled, Reece, Steven, Costanza, Enrico, Rodden, Tom and Jennings, Nicholas R. (2013) Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling At International Conference on Intelligent User Interfaces, United States. 19 - 22 Mar 2013. , pp. 383-394.

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: March 2013
Venue - Dates: International Conference on Intelligent User Interfaces, United States, 2013-03-19 - 2013-03-22
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 346991
URI: http://eprints.soton.ac.uk/id/eprint/346991
ISBN: 978-1-4503-1965-2
PURE UUID: 18dd881e-4889-4768-955c-15f66c567797
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Trung Dong Huynh: ORCID iD orcid.org/0000-0003-4937-2473

Catalogue record

Date deposited: 15 Jan 2013 09:43
Last modified: 10 Nov 2017 00:02

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Contributors

Author: Joel E. Fischer
Author: Sarvapali D. Ramchurn ORCID iD
Author: Michael A. Osborne
Author: Oliver Parson
Author: Trung Dong Huynh ORCID iD
Author: Muddasser Alam
Author: Nadia Pantidi
Author: Stuart Moran
Author: Khaled Bachour
Author: Steven Reece
Author: Enrico Costanza
Author: Tom Rodden
Author: Nicholas R. Jennings

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