TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

  • Authors:

  • Jun-Young Kwak;Pradeep Varakantham;Rajiv Maheswaran;Yu-Han Chang;Milind Tambe;Burcin Becerik-Gerber;Wendy Wood

  • Affiliations:

  • University of Southern California, Los Angeles, USA 90089;Singapore Management University, Singapore, Singapore 178902;University of Southern California, Los Angeles, USA 90089;University of Southern California, Los Angeles, USA 90089;University of Southern California, Los Angeles, USA 90089;University of Southern California, Los Angeles, USA 90089;University of Southern California, Los Angeles, USA 90089

  • Venue:
  • Autonomous Agents and Multi-Agent Systems
  • Year:
  • 2014

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Abstract

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA's key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program 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; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA's assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110,000 meetings held at nine campus buildings during an 8-month period in 2011---2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.