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AgentSwitch: towards smart electricity tariff selection

AgentSwitch: towards smart electricity tariff selection
AgentSwitch: towards smart electricity tariff selection
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the flow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
electricity, smart grid, optimisation, group buying, provenance, recommender systems
978-1-4503-1993-5
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Osborne, Michael
057f7c2b-60dc-4998-be8d-d0e52edbb33a
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Rahwan, Talal
476029f3-5484-4747-9f44-f63f3687083c
Maleki, Sasan
85222410-87d4-44eb-8721-fae2612b7721
Reece, Steve
9eecf0b5-3207-4067-9b72-6abcea5b54ab
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Alam, Muddasser
9a8f7f5d-2a65-4933-8903-d48445358eb8
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Roberts, Sephen
0d18bd3d-1128-43af-a55e-64075946edca
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Osborne, Michael
057f7c2b-60dc-4998-be8d-d0e52edbb33a
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Rahwan, Talal
476029f3-5484-4747-9f44-f63f3687083c
Maleki, Sasan
85222410-87d4-44eb-8721-fae2612b7721
Reece, Steve
9eecf0b5-3207-4067-9b72-6abcea5b54ab
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Alam, Muddasser
9a8f7f5d-2a65-4933-8903-d48445358eb8
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Rodden, Tom
b7d2e320-3783-4d67-93ff-c7b29dd8ba8e
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Roberts, Sephen
0d18bd3d-1128-43af-a55e-64075946edca

Ramchurn, Sarvapali, Osborne, Michael, Parson, Oliver, Rahwan, Talal, Maleki, Sasan, Reece, Steve, Huynh, Trung Dong, Alam, Muddasser, Fischer, Joel, Rodden, Tom, Moreau, Luc and Roberts, Sephen (2013) AgentSwitch: towards smart electricity tariff selection. 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), , Saint Paul, United States. 06 - 10 May 2013. 8 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the flow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.

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More information

e-pub ahead of print date: 8 May 2013
Venue - Dates: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), , Saint Paul, United States, 2013-05-06 - 2013-05-10
Keywords: electricity, smart grid, optimisation, group buying, provenance, recommender systems
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 349815
URI: http://eprints.soton.ac.uk/id/eprint/349815
ISBN: 978-1-4503-1993-5
PURE UUID: 928690a5-f47d-48de-bbb5-2ec84fe1a6df
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Trung Dong Huynh: ORCID iD orcid.org/0000-0003-4937-2473
ORCID for Luc Moreau: ORCID iD orcid.org/0000-0002-3494-120X

Catalogue record

Date deposited: 12 Mar 2013 09:44
Last modified: 15 Mar 2024 03:22

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Contributors

Author: Sarvapali Ramchurn ORCID iD
Author: Michael Osborne
Author: Oliver Parson
Author: Talal Rahwan
Author: Sasan Maleki
Author: Steve Reece
Author: Trung Dong Huynh ORCID iD
Author: Muddasser Alam
Author: Joel Fischer
Author: Tom Rodden
Author: Luc Moreau ORCID iD
Author: Sephen Roberts

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