Hybrid Bacterial Foraging Tabu Search Energy Optimization Technique in Smart Homes

  • Muhammad Usman Khalid
  • Nadeem JavaidEmail author
  • Muhammad Nadeem Iqbal
  • Aqib Jamil
  • Naveed Anwar
  • Qazi Muhammad Fazal E. Haq
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)


With the advent of the smart grid, it has become possible to improve the energy systems. To optimize the energy consumption pattern of the appliances, home energy management system is proposed for smart homes. Energy management in smart homes is a challenging task, therefore, the concept of demand-side management was introduced. For the effective scheduling of smart appliance, we propose a metaheuristic optimization technique. The proposed technique is hybrid of two existing techniques: Tabu Search (TS) and Bacterial Foraging Algorithm (BFA). The aim of the proposed technique is to reduce energy consumption so that user electricity bill reduces. Also, improves user comfort in term of average waiting time. For electricity bill calculation and appliance scheduling, time of use price tariff is used. Simulation results demonstrate that proposed scheme outperformed existing schemes in cost reduction and the average waiting time minimization. However, TS outruns other scheduling schemes in peak to average ratio reduction.


Smart grid Demand side management Time of use Tabu Search Bacterial Foraging Algorithm 


  1. 1.
    Demand Side Management (vol. 1, Overview of Key Issues), Final Rep. for RP2381-4, prepared by Battelle-Columbus Division and Synergic Resources Corp., EA/EM-3597. Electric Power Research institute, Palo Alto, CA, August 1984Google Scholar
  2. 2.
    U.S. Congress, Office of Technology Assessment 1993, Adapted From Battelle-Columbus Division and Synergic Resources Corp., Demand Side Management, Volume 3: Technology Alternatives and Market Implementation Methods EPRIIAVEM-3597. Eclectic Power Research Institute, Palo Alto, CA (1984)Google Scholar
  3. 3.
    Davito, B., Tai, H., Uhlaner, R.: The smart grid and the promise of demand-side management. McKinsey Smart Grid 3, 8–44 (2010)Google Scholar
  4. 4.
    Anvari-Moghaddam, A., Monsef, H., Rahimi-Kian, A.: Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans. Smart Grid 6(1), 324–332 (2015)CrossRefGoogle Scholar
  5. 5.
    Zhu, Z., Lambotharan, S., Chin, W.H., Fan, Z.: A game theoretic optimization framework for home demand management incorporating local energy resources. IEEE Trans. Ind. Inform. 11(2), 353–362 (2015)Google Scholar
  6. 6.
    Paterakis, N.G., Erdinc, O., Bakirtzis, A.G., Catalão, J.P.: Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies. IEEE Trans. Ind. Inform. 11(6), 1509–1519 (2015)CrossRefGoogle Scholar
  7. 7.
    Roh, H.T., Lee, J.W.: Residential demand response scheduling with multiclass appliances in the smart grid. IEEE Trans. Smart Grid 7(1), 94–104 (2016)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Graditi, G., Di Silvestre, M.L., Gallea, R., Sanseverino, E.R.: Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Trans. Ind. Inform. 11(1), 271–280 (2015)CrossRefGoogle Scholar
  10. 10.
    Ma, J., Chen, H.H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  11. 11.
    Basit, A., Sidhu, G.A.S., Mahmood, A., Gao, F.: Efficient and autonomous energy management techniques for the future smart homes. IEEE Trans. Smart Grid 8(2), 1–10 (2015)CrossRefGoogle Scholar
  12. 12.
    Zhao, C., Dong, S., Li, F., Song, Y.: Optimal home energy management system with mixed types of loads. CSEE J. Power Energy Syst. 1(4), 29–37 (2015)CrossRefGoogle Scholar
  13. 13.
    Joo, I.Y., Choi, D.H.: Optimal household appliance scheduling considering consumer’s electricity bill target. IEEE Trans. Consum. Electr. 63(1), 19–27 (2017)CrossRefGoogle Scholar
  14. 14.
    Joo, I.Y., Choi, D.H.: Distributed optimization framework for energy management of multiple smart homes with distributed energy resources. IEEE Access 5, 15551–15560 (2017)CrossRefGoogle Scholar
  15. 15.
    Shafiq, S., Fatima, I., Abid, S., Asif, S., Ansar, S., Abideen, Z.U., Javaid, N.: Optimization of home energy management system through application of tabu search. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 37–49. Springer, Cham, November 2017Google Scholar
  16. 16.
    Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z.A., Qasim, U.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 494–502. IEEE, July 2016Google Scholar
  17. 17.
    Passino, K.M.: Biomimicry of BFA for distributed optimization and control. Control Syst. IEEE 22(3), 52–67 (2002)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Muhammad Usman Khalid
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Muhammad Nadeem Iqbal
    • 2
  • Aqib Jamil
    • 1
  • Naveed Anwar
    • 3
  • Qazi Muhammad Fazal E. Haq
    • 2
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyWah CanttPakistan
  3. 3.University of WahWah CanttPakistan

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