Publication Type

Conference Proceeding Article

Publication Date



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 energyefficient 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.


Innovative Applications, Energy, Sustainable Multiagent Building Application, Energy-oriented Scheduling, Algorithms, Experimentation, Human Factors


Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics


AAMAS '13: Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems: May 6-10, 2013, St. Paul, MN, USA

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Last Page






City or Country

Richland, SC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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