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

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.

[1]  Shobha Venkataraman,et al.  Context-specific multiagent coordination and planning with factored MDPs , 2002, AAAI/IAAI.

[2]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[3]  Pradip Bose,et al.  Energy-aware meeting scheduling algorithms for smart buildings , 2012, BuildSys '12.

[4]  T. Ostrom,et al.  Repetition of highly similar messages and attitude change. , 1974 .

[5]  D. Stewart,et al.  Advertising Repetition: A Critical Review of Wearin and Wearout , 1988, Current Issues and Research in Advertising.

[6]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[7]  A. Carrico,et al.  Motivating energy conservation in the workplace: An evaluation of the use of group-level feedback and peer education , 2011 .

[8]  Brenda White,et al.  DEPARTMENT OF LABOR , 2006 .

[9]  Terry L. Zimmerman,et al.  Incremental Scheduling to Maximize Quality in a Dynamic Environment , 2006, ICAPS.

[10]  Milind Tambe,et al.  Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping , 2009, ICAPS.

[11]  R. Wets,et al.  Stochastic programming , 1989 .

[12]  E. Beale.  ON MINIMIZING A CONVEX FUNCTION SUBJECT TO LINEAR INEQUALITIES , 1955 .

[13]  Milind Tambe,et al.  SAVES: a sustainable multiagent application to conserve building energy considering occupants , 2012, AAMAS.

[14]  Jacques Wainer,et al.  Scheduling meetings through multi-agent negotiations , 2007, Decis. Support Syst..

[15]  Milind Tambe,et al.  Towards Adjustable Autonomy for the Real World , 2002, J. Artif. Intell. Res..

[16]  David T. Neal,et al.  The habitual consumer. , 2009 .

[17]  Sridhar Mahadevan,et al.  Hierarchical multi-agent reinforcement learning , 2001, AGENTS '01.

[18]  Gang Xiong,et al.  Smart (in-home) power scheduling for demand response on the smart grid , 2011, ISGT 2011.

[19]  Carol C. Menassa,et al.  Effect of social network type on building occupant energy use , 2012, BuildSys '12.

[20]  Sarvapali D. Ramchurn,et al.  Agent-based control for decentralised demand side management in the smart grid , 2011, AAMAS.

[21]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[22]  Vijay Arya,et al.  User-sensitive scheduling of home appliances , 2011, GreenNets '11.

[23]  Jacques Wainer,et al.  Scheduling Meetings through Multi-agent Negotiation , 2000, IBERAMIA-SBIA.

[24]  Chen Wang,et al.  A Service-Oriented System for Optimizing Residential Energy Use , 2009, 2009 IEEE International Conference on Web Services.

[25]  J. Leigh,et al.  Current issues and research in advertising , 1978 .

[26]  Evan Sultanik,et al.  On Modeling Multiagent Task Scheduling as a Distributed Constraint Optimization Problem , 2007, IJCAI.

[27]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[28]  Alexander Shapiro,et al.  Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications , 2009, J. Optimization Theory and Applications.

[29]  John T. Cacioppo,et al.  Effects of message Repetition on Argument Processing, Recall, and Persuasion , 1989 .

[30]  Milind Tambe,et al.  Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[31]  Willett Kempton,et al.  Deploying power grid-integrated electric vehicles as a multi-agent system , 2011, AAMAS.

[32]  George B. Dantzig,et al.  Linear Programming Under Uncertainty , 2004, Manag. Sci..

[33]  Rajiv T. Maheswaran,et al.  Improving building energy efficiency with a network of sensing, learning and prediction agents , 2012, AAMAS.

[34]  B DantzigGeorge.  Linear Programming Under Uncertainty , 1955 .

[35]  Alexander Shapiro,et al.  The Sample Average Approximation Method for Stochastic Discrete Optimization , 2002, SIAM J. Optim..

[36]  R. Baron,et al.  The relation between distraction and persuasion. , 1973 .

[37]  Milind Tambe,et al.  Towards Robust Multi-objective Optimization Under Model Uncertainty for Energy Conservation , 2012 .

[38]  Nicholas R. Jennings,et al.  A model-based online mechanism with pre-commitment and its application to electric vehicle charging , 2012, AAMAS.

[39]  Didier Dubois,et al.  Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge , 2003, Eur. J. Oper. Res..

[40]  A. Shapiro,et al.  The Sample Average Approximation Method for Stochastic Programs with Integer Recourse , 2002 .

[41]  Alexander Shapiro,et al.  Lectures on Stochastic Programming: Modeling and Theory , 2009 .

[42]  Milind Tambe,et al.  TESLA: an energy-saving agent that leverages schedule flexibility , 2013, AAMAS.

[43]  Ahmad Faruqui,et al.  The impact of informational feedback on energy consumption d A survey of the experimental evidence , 2010 .

[44]  Sarvapali D. Ramchurn,et al.  Optimal decentralised dispatch of embedded generation in the smart grid , 2012, AAMAS.