In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. Agent-Switch 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 of 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.
['Sarvapali D. Ramchurn', 'Michael A. Osborne', 'Oliver Parson', 'Talal Rahwan', 'Sasan Maleki', 'Steven Reece', 'Trung Dong Huynh', 'Muddasser Alam', 'Joel E. Fischer', 'Tom Rodden', 'Luc Moreau', 'Steve G. Roberts']
AgentSwitch: towards smart energy tariff selection