Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs


  • Valentin Robu Heriot-Watt University
  • Meritxell Vinyals University of Southampton
  • Alex Rogers University of Southampton
  • Nicholas Jennings University of Southampton




smart grids, electricity tariffs, group buying, coalition structure


Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customer's consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work {aamas2014} studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK.




How to Cite

Robu, V., Vinyals, M., Rogers, A., & Jennings, N. (2014). Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8764



Computational Sustainability and Artificial Intelligence