Preemptive Appliances Scheduling in Smart Home Using Genetic Algorithm

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

Smart grid is the newer version of electricity network that incorporates the information and communication technology in addition with the renewable energy resources. Smart grid offers electricity for the users at different rates at different time. Smart grid employs smart home where there is a set of electrical appliances that need to be scheduled in such a way to reduce their electricity charges. Many bio-inspired algorithms are of greater interest for the recent years and are being able to solve several complex problems in the real-world situations; the smart home appliances scheduling problem is solved using the genetic algorithm. Two new operators namely appliance-based one-point crossover operator and appliance-based rotation mutation operator are used for solving the problem.

Keywords

Smart grid Genetic algorithm Scheduling 

References

  1. 1.
    X. Fang, S. Misra, G. Xue, D. Yang, Smart grid-the new and improved power grid: a survey. IEEE Commun. Surv. tutorials. 14(13) (2012)Google Scholar
  2. 2.
    Y.-C. Huang, C.-M. Huang, K.-Y. Huang, C.-Y. Liu, Energy optimization approaches for smart home applications. Internal Conference on International Conference on Artificial Intelligence and Soft computing, Lecture Notes in Information Technology. vol. 12 (2012)Google Scholar
  3. 3.
    Z. Zhao, W.C. Lee, Y. Shin, K.-B. Song, An optimal power scheduling method applied in home energy management system based on demand response. J. Electron. Telecommun. Res. Inst. (ETRI) 35, 677–686 (2013)Google Scholar
  4. 4.
    X. Chen, T. Wei, S. Hu, Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home. IEEE Trans Smart Grid. (2013)Google Scholar
  5. 5.
    S. Chen, P. Sinha, N.B. Shroff, Heterogeneous delay tolerant task scheduling and energy management in the smart grid with renewable energy. IEEE J. Sel. Areas Commu. 31 (2013)Google Scholar
  6. 6.
    A.-H. Mohsenian-Rad, V.W.S. Wong, J. Jatskevich, R. Schober, A. Leon-Garcia, Autonomous demand side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid. 1 (2010)Google Scholar
  7. 7.
    C. Joe, S. Sen, S. Ha, M. Chiang, Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility. IEEE J Sel Areas Commu. 30 (2012)Google Scholar
  8. 8.
    W. Saad, Z. Han, H.V. Poor, T. Basar, Game—theoretic methods for the smart grid. IEEE Signal Process. Mag. 29(5) (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Bharathidasan UniversityTiruchirappalliIndia

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