Home Energy Management Using Social Spider and Bacterial Foraging Algorithm

  • Arje Saba
  • Adia Khalid
  • Adnan Ishaq
  • Komal Parvez
  • Sayed Aimal
  • Waqar Ali
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)

Abstract

Electricity is a controllable and convenient form of energy. In this paper we discus about the electricity control. In current years Demand Side Management (DSM) techniques are designed. For residential and commercial sectors. These techniques are very effective to control the load profile of customer in grid area network. In this paper we use two optimization techniques: Harmony Search Algorithm (HSA) and Firefly Algorithm (FA). In our work we categorize smart appliances in three different categories on the basis of their energy consumption. For energy pricing we use Time of Use (ToU)pricing signal. Simulation result verify our adopted approach significantly reduce the cost without compromise the user comfort.

References

  1. 1.
    Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Buildings 129, 452–470 (2016)CrossRefGoogle Scholar
  2. 2.
    Safdarian, A., et al.: Optimal residential load management in smart grids: A decentralized framework. IEEE Trans. Smart Grid 7(4), 1836–1845 (2016)CrossRefGoogle Scholar
  3. 3.
    Liu, Y., et al.: Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)CrossRefGoogle Scholar
  4. 4.
    Ma, J., et al.: Residential load scheduling in smart grid: A cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  5. 5.
    Zhao, Z., et al.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  6. 6.
    Zhu, Z., et al.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT). IEEE (2012)Google Scholar
  7. 7.
    Javaid, N., et al.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  8. 8.
    Samadi, P., Wong, V.W.S., Schober, R.: Load scheduling and power trading in systems with high penetration of renewable energy resources. IEEE Trans. Smart Grid 7(4), 1802–1812 (2016)CrossRefGoogle Scholar
  9. 9.
    Ullah, I., et al.: An incentive-based optimal energy consumption scheduling algorithm for residential users. Procedia Comput. Sci. 52, 851–857 (2015)CrossRefGoogle Scholar
  10. 10.
    Rasheed, M.B., et al.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5(4), 1134–1163 (2015)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Moghaddam, M.H.Y., et al.: On the performance of distributed and cloud-based demand response in smart grid. IEEE Trans. Smart Grid PP(99), 1 (2017)Google Scholar
  12. 12.
    Geem, Z.W., et al.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  13. 13.
    Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008)Google Scholar
  14. 14.
    Ali, N., et al.: A review of firefly algorithm. ARPN J. Eng. Appl. Sci. 9(10) (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Arje Saba
    • 1
  • Adia Khalid
    • 1
  • Adnan Ishaq
    • 1
  • Komal Parvez
    • 1
  • Sayed Aimal
    • 1
  • Waqar Ali
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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