Bayesian quadrature with non-normal approximating functions
Statistics and Computing
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Modelling the provenance of data in autonomous systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Polynomial calculation of the Shapley value based on sampling
Computers and Operations Research
The design of eco-feedback technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A scoring rule-based mechanism for aggregate demand prediction in the smart grid
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
AgentSwitch: towards smart energy tariff selection
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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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.