Scheduling meetings through multi-agent negotiations
Decision Support Systems
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
User-sensitive scheduling of home appliances
Proceedings of the 2nd ACM SIGCOMM workshop on Green networking
SAVES: a sustainable multiagent application to conserve building energy considering occupants
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A model-based online mechanism with pre-commitment and its application to electric vehicle charging
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
TESLA: an extended study of an energy-saving agent that leverages schedule flexibility
Autonomous Agents and Multi-Agent Systems
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This innovative application paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA's key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people's preferences for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA's assumptions exist in practice. TESLA was evaluated on data of over 110,000 meetings held at nine campus buildings during eight months in 2011-2012 at USC and SMU. These results show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.