Advertisement

Home Energy Management Using Hybrid Meta-heuristic Optimization Technique

  • Orooj Nazeer
  • Nadeem Javaid
  • Adnan Ahmed Rafique
  • Sajid Kiani
  • Yasir Javaid
  • Zeeshan Khurshid
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)

Abstract

Home energy management systems have been widely used for energy management in smart homes. Management of energy in smart home is a difficult task and requires efficient scheduling for smart appliances in a home. A meta-heuristic optimization technique is proposed in this paper. The proposed Harmony Search Gray Wolf Optimization (HSGWO) is a hybrid of Harmony Search Algorithm (HSA) and Gray Wolf Optimization (GWO). The pricing signal used for the calculation of electricity cost is Real Time Pricing (RTP). The basic aim of this paper is to reduce electricity cost, Peak to Average Ratio (PAR) and maximization of user comfort. Simulation results show that HSGWO performs better as compared to HSA and GWO. The findings demonstrate that there is a trade off between electricity cost and user comfort.

References

  1. 1.
    Zhang, D., Li, S., Sun, M., O’Neill, Z.: An optimal and learning-based demand response and home energy management system. IEEE Trans. Smart Grid 7(4), 1790–1801 (2016)CrossRefGoogle Scholar
  2. 2.
    Shafie-khah, M., Siano, P.: A stochastic home energy management system considering satisfaction cost and response fatigue. IEEE Trans. Ind. Inform. (2017)Google Scholar
  3. 3.
    Akhavan-Rezai, E., Shaaban, M.F., El-Saadany, E.F., Karray, F.: Online intelligent demand management of plug-in electric vehicles in future smart parking lots. IEEE Syst. J. 10(2), 483–494 (2016)CrossRefGoogle Scholar
  4. 4.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Jayabarathi, T., Raghunathan, T., Adarsh, B.R., Suganthan, P.N.: Economic dispatch using hybrid grey wolf optimizer. Energy 111, 630–641 (2016)CrossRefGoogle Scholar
  6. 6.
    Geem, Z.W., Yoon, Y.: Harmony search optimization of renewable energy charging with energy storage system. Int. J. Electr. Power Energy Syst. 86, 120–126 (2017)CrossRefGoogle Scholar
  7. 7.
    Ouyang, H.B., Gao, L.Q., Kong, X.Y., Li, S., Zou, D.X.: Hybrid harmony search particle swarm optimization with global dimension selection. Inf. Sci. 346, 318–337 (2016)CrossRefGoogle Scholar
  8. 8.
    Ambia, M.N., Hasanien, H.M., Al-Durra, A., Muyeen, S.M.: Harmony search algorithm-based controller parameters optimization for a distributed-generation system. IEEE Trans. Power Delivery 30(1), 246–255 (2015)CrossRefGoogle Scholar
  9. 9.
    Manzoor, A., Javaid, N., Ullah, I., Abdul, W., Almogren, A., Alamri, A.: An intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes. Energies 10(9), 1258 (2017)CrossRefGoogle Scholar
  10. 10.
    Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  11. 11.
    Mahmood, D., Javaid, N., Ahmed, I., Alrajeh, N., Niaz, I.A. Khan, Z.A.: Multi-agent-based sharing power economy for a smart community. Int. J. Energy Res. 41, 2074–2090 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhao, Z., Lee, W.C., Shin, Y., Song, K.B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Rajalingam, S., Malathi, V.: HEM algorithm based smart controller for home power management system. Energy Build. 131, 184–192 (2016)CrossRefGoogle Scholar
  15. 15.
    Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build. 138, 215–227 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Orooj Nazeer
    • 1
  • Nadeem Javaid
    • 2
  • Adnan Ahmed Rafique
    • 3
  • Sajid Kiani
    • 4
  • Yasir Javaid
    • 5
  • Zeeshan Khurshid
    • 1
  1. 1.Department of Computing and TechnologyAbasyn UniversityIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyIslamabadPakistan
  3. 3.University of PoonchRawalakotPakistan
  4. 4.Allama Iqbal Open UniversityIslamabadPakistan
  5. 5.Government College of Technology (TEVTA)RawalakotPakistan

Personalised recommendations