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The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach

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Book cover PRIMA 2020: Principles and Practice of Multi-Agent Systems (PRIMA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12568))

Abstract

Daily energy demand peaks induce high greenhouse gas emissions and are deleterious to the power grid operations. The autonomous and coordinated control of smart appliances in residential buildings represents an effective solution to reduce peak demands. This coordination problem is challenging as it involves, not only, scheduling devices to minimize energy peaks, but also to comply with user’ preferences. Prior work assumed these preferences to be fully specified and known a priori, which is, however, unrealistic. To remedy this limitation, this paper introduces a Bayesian optimization approach for smart appliance scheduling when the users’ satisfaction with a schedule must be elicited, and thus considered expensive to evaluate. The paper presents a set of ad-hoc energy-cost based acquisition functions to drive the Bayesian optimization problem to find schedules that maximize the user’s satisfaction. The experimental results demonstrate the effectiveness of the proposed energy-cost based acquisition functions which improve the algorithm’s performance up to 26%.

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Notes

  1. 1.

    California ISO (CAISO). https://tinyurl.com/y8t4xa2r.

  2. 2.

    Pecan Street Inc. https://www.pecanstreet.org/dataport/.

References

  1. Amini, M.H., Frye, J., Ilić, M.D., Karabasoglu, O.: Smart residential energy scheduling utilizing two stage mixed integer linear programming. In: North American Power Symposium (2015)

    Google Scholar 

  2. Chen, L., Pu, P.: Survey of preference elicitation methods. Technical report, Swiss Federal Institute of Technology in Lausanne (EPFL) (2004)

    Google Scholar 

  3. Chen, Z., Wu, L., Fu, Y.: Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans. Smart Grid 3(4), 1822–1831 (2012)

    Article  Google Scholar 

  4. Du, P., Lu, N.: Appliance commitment for household load scheduling. IEEE Trans. Smart Grid 2(2), 411–419 (2011)

    Article  Google Scholar 

  5. Duvenaud, D.: The kernel cookbook. In: Advice on Covariance Functions, pp. 12–39 (2015)

    Google Scholar 

  6. Fang, X., Misra, S., Xue, G., Yang, D.: Smart grid-the new and improved power grid: a survey. IEEE Commun. Surv. Tutor. 14(4), 944–980 (2011)

    Article  Google Scholar 

  7. Farhangi, H.: The path of the smart grid. IEEE Power Energy Mag. 8(1), 18–28 (2009)

    Article  Google Scholar 

  8. Fioretto, F., Yeoh, W., Pontelli, E.: A multiagent system approach to scheduling devices in smart homes. In: Proceedings of AAMAS, pp. 981–989 (2017)

    Google Scholar 

  9. Frazier, P.I.: A tutorial on Bayesian optimization. CoRR abs/1807.02811 (2018)

    Google Scholar 

  10. Gelazanskas, L., Gamage, K.A.: Demand side management in smart grid: a review and proposals for future direction. Sustain. Cities Soc. 11, 22–30 (2014)

    Article  Google Scholar 

  11. Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Glob. Optim. 21(4), 345–383 (2001)

    Article  MathSciNet  Google Scholar 

  12. Le, T., Tabakhi, A.M., Tran-Thanh, L., Yeoh, W., Son, T.C.: Preference elicitation with interdependency and user bother cost. In: Proceedings of AAMAS, pp. 1459–1467 (2018)

    Google Scholar 

  13. Lizotte, D.J.: Practical Bayesian optimization. University of Alberta (2008)

    Google Scholar 

  14. Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)

    Article  Google Scholar 

  15. Mockus, J., Mockus, L.: Bayesian approach to global optimization and application to multiobjective and constrained problems. J. Optim. Theory Appl. 70(1), 157–172 (1991)

    Article  MathSciNet  Google Scholar 

  16. Mockus, J.: On Bayesian methods for seeking the extremum and their application. In: Proceedings of Information Processing, pp. 195–200 (1977)

    Google Scholar 

  17. Mockus, J.: Application of Bayesian approach to numerical methods of global and stochastic optimization. J. Global Optim. 4(4), 347–365 (1994)

    Article  MathSciNet  Google Scholar 

  18. Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. Towards Global Optim. 2(117–129), 2 (1978)

    MATH  Google Scholar 

  19. Mohsenian-Rad, A.H., Wong, V.W., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010)

    Article  Google Scholar 

  20. Nguyen, V., Yeoh, W., Son, T.C., Kreinovich, V., Le, T.: A scheduler for smart homes with probabilistic user preferences. In: Proceedings of PRIMA, pp. 138–152 (2019)

    Google Scholar 

  21. Peeters, L., De Dear, R., Hensen, J., D’haeseleer, W.: Thermal comfort in residential buildings: Comfort values and scales for building energy simulation. Applied energy 86(5), 772–780 (2009)

    Google Scholar 

  22. Rhodes, J.D., et al.: Experimental and data collection methods for a large-scale smart grid deployment: methods and first results. Energy 65, 462–471 (2014)

    Article  Google Scholar 

  23. Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming. Elsevier, Amsterdam (2006)

    Google Scholar 

  24. Rossi, F., Petrie, C.J., Dhar, V.: On the equivalence of constraint satisfaction problems. In: European Conference on Artificial Intelligence, pp. 550–556 (1990)

    Google Scholar 

  25. Rust, P., Picard, G., Ramparany, F.: Using message-passing DCOP algorithms to solve energy-efficient smart environment configuration problems. In: Proceedings of IJCAI, pp. 468–474 (2016)

    Google Scholar 

  26. Samadi, P., Mohsenian-Rad, H., Schober, R., Wong, V.W.: Advanced demand side management for the future smart grid using mechanism design. IEEE Trans. Smart Grid 3(3), 1170–1180 (2012)

    Article  Google Scholar 

  27. Scott, P., Thiébaux, S., Van Den Briel, M., Van Hentenryck, P.: Residential demand response under uncertainty. In: International Conference on Principles and Practice of Constraint Programming, pp. 645–660 (2013)

    Google Scholar 

  28. Sedghi, M., Atia, G., Georgiopoulos, M.: Kernel coherence pursuit: a manifold learning-based outlier detection technique. In: Proceedings of ACSSC, pp. 2017–2021 (2018)

    Google Scholar 

  29. Shann, M., Seuken, S.: An active learning approach to home heating in the smart grid. In: Proceedings of IJCAI, pp. 2892–2899 (2013)

    Google Scholar 

  30. Song, W., Kang, D., Zhang, J., Cao, Z., Xi, H.: A sampling approach for proactive project scheduling under generalized time-dependent workability uncertainty. J. Artif. Intell. Res. 64, 385–427 (2019)

    Article  MathSciNet  Google Scholar 

  31. Sou, K.C., Weimer, J., Sandberg, H., Johansson, K.H.: Scheduling smart home appliances using mixed integer linear programming. In: Proceedings of CDC, pp. 5144–5149 (2011)

    Google Scholar 

  32. Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Gaussian process optimization in the bandit setting: no regret and experimental design. In: Proceedings of ICML, pp. 1015–1022 (2010)

    Google Scholar 

  33. Stuckey, P.J., et al..: The evolving world of MiniZinc. In: Constraint Modelling and Reformulation, pp. 156–170 (2007)

    Google Scholar 

  34. Tabakhi, A.M.: Preference elicitation in DCOPs for scheduling devices in smart buildings. In: Proceedings of AAAI, pp. 4989–4990 (2017)

    Google Scholar 

  35. Tabakhi, A.M., Le, T., Fioretto, F., Yeoh, W.: Preference elicitation for DCOPs. In: Proceedings of CP, pp. 278–296 (2017)

    Google Scholar 

  36. Tabakhi, A.M., Yeoh, W., Yokoo, M.: Parameterized heuristics for incomplete weighted CSPs with elicitation costs. In: Proceedings of AAMAS, pp. 476–484 (2019)

    Google Scholar 

  37. Truong, N.C., Baarslag, T., Ramchurn, S.D., Tran-Thanh, L.: Interactive scheduling of appliance usage in the home. In: Proceedings of IJCAI, pp. 869–877 (2016)

    Google Scholar 

  38. Williams, C.K., Rasmussen, C.E.: Gaussian Processes for Machine Learning, vol. 2. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  39. Wilson, J., Hutter, F., Deisenroth, M.: Maximizing acquisition functions for Bayesian optimization. In: Advances in Neural Information Processing Systems (2018)

    Google Scholar 

  40. Xiao, Y., Tabakhi, A.M., Yeoh, W.: Embedding preference elicitation within the search for DCOP solutions. In: Proceedings of AAMAS, pp. 2044–2046 (2020)

    Google Scholar 

  41. Zhu, J., Lin, Y., Lei, W., Liu, Y., Tao, M.: Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm. Energy 171, 944–955 (2019)

    Article  Google Scholar 

  42. Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: IEEE PES Innovative Smart Grid Technologies Conference, pp. 1–5 (2012)

    Google Scholar 

  43. Žilinskas, A.: On the use of statistical models of multimodal functions for the construction of the optimization algorithms. In: Iracki, K., Malanowski, K., Walukiewicz, S. (eds.) Optimization Techniques, pp. 138–147. Springer, Heidelberg (1980). https://doi.org/10.1007/BFb0006597

    Chapter  Google Scholar 

  44. Žilinskas, A.: A review of statistical models for global optimization. J. Global Optim. 2(2), 145–153 (1992)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

Tabakhi and Yeoh are partially supported by NSF grants 1550662 and 1812619, and Fioretto is partially supported by NSF grant 2007164. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.

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Authors and Affiliations

  1. Washington University in St. Louis, St. Louis, MO, USA

    Atena M. Tabakhi & William Yeoh

  2. Syracuse University, Syracuse, NY, USA

    Ferdinando Fioretto

Authors
  1. Atena M. Tabakhi
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  2. William Yeoh
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  3. Ferdinando Fioretto
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Correspondence to Atena M. Tabakhi .

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Editors and Affiliations

  1. Nagoya Institute of Technology, Nagoya, Japan

    Takahiro Uchiya

  2. University of Tasmania, Tasmania, TAS, Australia

    Quan Bai

  3. University of Alcalá, Alcala de Henares, Spain

    Iván Marsá Maestre

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Tabakhi, A.M., Yeoh, W., Fioretto, F. (2021). The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach. In: Uchiya, T., Bai, Q., Marsá Maestre, I. (eds) PRIMA 2020: Principles and Practice of Multi-Agent Systems. PRIMA 2020. Lecture Notes in Computer Science(), vol 12568. Springer, Cham. https://doi.org/10.1007/978-3-030-69322-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-69322-0_7

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