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

Autonomous Agents and Multi-Agent Systems

Volumn 28, Issue 4, 2014, Pages 605-636

  Kwak, Jun young ^a   Varakantham, Pradeep ^b   Maheswaran, Rajiv ^a   Chang, Yu Han ^a   Tambe, Milind ^a   Becerik Gerber, Burcin ^a   Wood, Wendy ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

Author keywords

Energy; Energy oriented scheduling; Scheduling flexibility; Sustainable multiagent systems

Indexed keywords

COMMERCIAL BUILDING; ENERGY; ENERGY-EFFICIENT SCHEDULING; MIXED INTEGER LINEAR PROGRAM; ONLINE SCHEDULING ALGORITHM; OPTIMIZING ENERGY; SCHEDULING FLEXIBILITY; UNIVERSITY OF SOUTHERN CALIFORNIA;

ENERGY UTILIZATION; INTEGER PROGRAMMING; MULTI AGENT SYSTEMS; OFFICE BUILDINGS; SCHEDULING; SCHEDULING ALGORITHMS;

ENERGY CONSERVATION;

EID: 84893774784     PISSN: 13872532     EISSN: 15737454     Source Type: Journal    
DOI: 10.1007/s10458-013-9234-0     Document Type: Article

References (46)

1. Abrahmase, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25, 273-291.
2. Ahmed, S., Shapiro, A., & Shapiro, E. (2002). The sample average approximation method for stochastic programs with integer recourse. SIAM Journal of Optimization, 12, 479-502.
3. Anderson, K., Lee, S., & Menassa, C. (2012). Effect of social network type on building occupant energy use. In Buildsys (pp. 17-24). New York: ACM.
4. Bapat, T., Sengupta, N., Ghai, S. K., Arya, V., Shrinivasan, Y. B., & Seetharam, D. (2011). User-sensitive scheduling of home appliances. In Proceedings of the 2nd ACM SIGCOMM workshop on Green networking (pp. 43-48). ACM.
5. Baron, R., Baron, P., & Miller, N. (1973). The relation between distraction and persuasion. Psychological Bulletin, 80(4), 310-323.
6. Beale, E. (1955). On minimizing a convex function subject to linear inequalities. Journal of the Royal Statistical Society. Series B (Methodological), 17, 173-184.
7. Cacioppo, J., & Petty, R. (1989). Effects of message repetition on argument processing, recall, and persuasion. Basic and Applied Social Psychology, 10(1), 3-12.
8. Carrico, A., & Riemer, M. (2011). Motivating energy conservation in the workplace: An evaluation of the use of group-level feedback and peer education. Journal of Environmental Psychology, 31(8-9), 1257-1274.
9. Dantzig, G. B. (1955). Linear programming under uncertainty. Management Science, 1(3-4), 197-206.
10. Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147(2), 231-252.
11. Faruqui, A., Sergici, S., & Sharif, A. (2010). The impact of informational feedback on energy consumption-A survey of the experimental evidence. Energy, 35(4), 1598-1608.
12. Gallagher, A., Zimmerman, T. L., & Smith, S. F. (2006). Incremental scheduling to maximize quality in a dynamic environment. In ICAPS (pp. 222-232).
13. Ghavamzadeh, M., Mahadevan, S., & Makar, R. (2006). Hierarchical multi-agent reinforcement learning. Autonomous Agents and Multi-Agent Systems, 13(2), 197-229.
14. Guestrin, C., Venkataraman, S., & Koller, D. (2002). Context-specific multiagent coordination and planning with factored MDPs. In AAAI/IAAI (pp. 253-259).
15. INFOCOM. (2001). Meetings in America: A study of trends, costs, and attitudes toward business travel and teleconferencing, and their impact on productivity. Whitepaper, INFOCOM.
16. Kall, P., & Wallace, S. W. (1994). Stochastic programming. Chichester: Wiley.
17. Kamboj, S., Kempton, W., & Decker, K. S. (2011). Deploying power grid-integrated electric vehicles as a multi-agent system. In The 10th international conference on autonomous agents and multiagent systems (Vol. 1, pp. 13-20). International Foundation for Autonomous Agents and Multiagent Systems.
18. Kelso, J. D. (Ed.). (2011). Buildings energy data book. U. S. Dept. of Energy.
19. Kwak, J., Varakantham, P., Maheswaran, R., Chang, Y., Tambe, M., Becerik-Gerber, B., et al. (2013). TESLA: An energy-saving agent that leverages schedule flexibility. In Proceedings of the 2013 international conference on autonomous agents and multi-agent systems (pp. 965-972). International Foundation for Autonomous Agents and Multiagent Systems.
20. Kwak, J., Varakantham, P., Maheswaran, R., Tambe, M., Hayes, T., Wood, W., & Becerik-Gerber, B. (2012). Towards robust multi-objective optimization under model uncertainty for energy conservation. In AAMAS workshop on agent technologies for energy systems (ATES).
21. Kwak, J., Varakantham, P., Maheswaran, R., Tambe, M., Jazizadeh, F., Kavulya, G., et al. (2012). SAVES: A sustainable multiagent application to conserve building energy considering occupants. In Proceedings of the 11th international conference on autonomous agents and multiagent systems (Vol. 1, pp. 21-28). International Foundation for Autonomous Agents and Multiagent Systems.
22. Maheswaran, R. T., Tambe, M., Bowring, E., Pearce, J. P., & Varakantham, P. (2004). Taking dcop to the real world: Efficient complete solutions for distributed multi-event scheduling. In Proceedings of the third international joint conference on autonomous agents and multiagent systems (Vol. 1, pp. 310-317). Washington, DC: IEEE Computer Society.
23. Majumdar, A., Albonesi, D. H., & Bose, P. (2012). Energy-aware meeting scheduling algorithms for smart buildings. In Buildsys (pp. 161-168). ACM.
24. Mamidi, S., Chang, Y. H., & Maheswaran, R. (2012). Improving building energy efficiency with a network of sensing, learning and prediction agents. In AAMAS.
25. McCullough, J., & Ostrom, T. (1974). Repetition of highly similar messages and attitude change. Journal of Applied Psychology, 59(3), 395-397.
26. Michigan State University. (2009). New classroom scheduling methods save energy, money for msu. http://news. msu. edu/story/6501/.
27. Miller, S., Ramchurn, S. D., & Rogers, A. (2012). Optimal decentralised dispatch of embedded generation in the smart grid. In AAMAS.
28. Mohsenian-Rad, A. H., & Leon-Garcia, A. (2010). Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transaction on Smart Grid, 1(2), 120-133.
29. Pagnoncelli, B., Ahmed, S., & Shapiro, A. (2009). Sample average approximation method for chance constrained programming: Theory and applications. Journal of Optimization Theory and Applications, 142(2), 399-416.
30. Pechmann, C., & Stewart, D. W. (1988). Advertising repetition: A critical review of wearin and wearout. Current Issues and Research in Advertising, 11(1-2), 285-329.
31. Policella, N., Smith, S. F., Cesta, A., & Oddi, A. (2004). Incremental scheduling to maximize quality in a dynamic environment. In ICAPS.
32. Portland State University. (2012). Efficient class scheduling conserves energy. http://goo. gl/cZwgB.
33. Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. (2011). Agent-based control for decentralised demand side management in the smart grid. In The 10th international conference on autonomous agents and multiagent systems (Vol. 1, pp. 5-12). International Foundation for Autonomous Agents and Multiagent Systems.
34. Scerri, P., Pynadath, D. V., & Tambe, M. (2002). Towards adjustable autonomy for the real world. JAIR, 17, 171-228.
35. Shapiro, A., Dentcheva, D., & Ruszczyński, A. (2009). Lectures on stochastic programming: Modeling and theory (Vol. 9). Society for Industrial Mathematics.
36. Sou, K. C., Weimer, J., Sandberg, H., & Johansson, K. H. (2011). Scheduling smart home appliances using mixed integer linear programming. In 50th IEEE conference on decision and control and european control conference (CDC-ECC) (pp. 5144-5149). IEEE.
37. Stein, S., Gerding, E., Robu, V., & Jennings, N. R. (2012). A model-based online mechanism with pre-commitment and its application to electric vehicle charging. In Proceedings of the 11th international conference on autonomous agents and multiagent systems (Vol. 2, pp. 669-676). International Foundation for Autonomous Agents and Multiagent Systems.
38. Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419-4426.
39. Subramanyam, S., & Askin, R. G. (1985). An expert systems approach to scheduling in flexible manufacturing systems. Ph. D. thesis, University of Iowa.
40. Sultanik, E., Modi, P. J., & Regli, W. C. (2007). On modeling multiagent task scheduling as a distributed constraint optimization problem. In Proceedings of the 20th international joint conference on, artificial intelligence (pp. 1531-1536).
41. U. S. Department of Labor. (2012). Average energy prices in the los angeles area. http://www. bls. gov/ro9/cpilosa_energy. htm.
42. Varakantham, P., Kwak, J., Taylor, M. E., Marecki, J., Scerri, P., & Tambe, M. (2009). Exploiting coordination locales in distributed POMDPS via social model shaping. In ICAPS.
43. Wainer, J., Jr., Ferreira, P. R., & Constantino, E. R. (2007). Scheduling meetings through multi-agent negotiations. Decision Support Systems, 44(1), 285-297.
44. Wang, C., de Groot, M., & Marendy, P. (2009). A service-oriented system for optimizing residential energy use. In IEEE international conference on web services, ICWS 2009 (pp. 735-742). IEEE.
45. Wood, W., & Neal, D. (2009). The habitual consumer. Journal of Consumer Psychology, 19, 579-592.
46. Xiong, G., Chen, C., Kishore, S., & Yener, A. (2011). Smart (in-home) power scheduling for demand response on the smart grid. In Innovative smart grid technologies (ISGT), 2011 IEEE PES (pp. 1-7). IEEE.