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
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Osborne, Michael
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Parson, Oliver
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Rahwan, Talal
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Maleki, Sasan
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Reece, Steve
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Huynh, Trung Dong
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Alam, Muddasser
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Fischer, Joel
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Rodden, Tom
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Moreau, Luc
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Roberts, Sephen
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Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Osborne, Michael
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Parson, Oliver
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Rahwan, Talal
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Maleki, Sasan
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Reece, Steve
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Huynh, Trung Dong
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Alam, Muddasser
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Fischer, Joel
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Rodden, Tom
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Moreau, Luc
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Roberts, Sephen
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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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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
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Date deposited: 12 Mar 2013 09:44
Last modified: 15 Mar 2024 03:22
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Contributors
Author:
Sarvapali Ramchurn
Author:
Michael Osborne
Author:
Oliver Parson
Author:
Talal Rahwan
Author:
Sasan Maleki
Author:
Steve Reece
Author:
Trung Dong Huynh
Author:
Muddasser Alam
Author:
Joel Fischer
Author:
Tom Rodden
Author:
Luc Moreau
Author:
Sephen Roberts
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