AgentSwitch: Towards Smart Energy Tariff Selection

Homeowners face a major challenge in choosing their optimal electricity tariff by having to predict their yearly energy consumption and then optimise their usage according to the complex pricing structure built into such tariffs. To address this challenge, we develop AgentSwitch, a prototype agent-based platform to solve the tariff selection problem. AgentSwitch incorporates novel algorithms that work on the coarse data provided by smart meters 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. Moreover, 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.