Differential-Evolution-Earthworm Hybrid Meta-heuristic Optimization Technique for Home Energy Management System in Smart Grid

  • Nadeem JavaidEmail author
  • Ihtisham Ullah
  • Syed Shahab Zarin
  • Mohsin Kamal
  • Babatunji Omoniwa
  • Abdul Mateen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)


In recent years, advanced technology is increasing rapidly, especially in the field of smart grids. A home energy management systems are implemented in homes for scheduling of power for cost minimization. In this paper, for management of home energy we propose a meta-heuristic technique which is hybrid of existing techniques enhanced differential evolution (EDE) and earthworm optimization algorithm (EWA) and it is named as earthworm EWA (EEDE). Simulations show that EWA performed better in term of reducing cost and EDE performed better in reducing peak to average ratio (PAR). However proposed scheme outperformed in terms of both cost and PAR. For evaluating the performance of proposed technique a home energy system proposed by us. In our work we are considering a single home, consists of many appliances. Appliances are categorized into two groups: Interruptible and un-interruptible. Simulations and results show that both algorithms performed well in terms of reducing costs and PAR. We also measured waiting time to find out user comfort and energy consumption.


EDE algorithm EWA algorithm User comfort Hybrid meta-heuristic technique 


  1. 1.
    Rasheed, M.B., et al.: Energy optimization in smart homes using customer preference and dynamic pricing. Energies 9(8), 1–25 (2016)CrossRefGoogle Scholar
  2. 2.
    Logenthiran, T., Member, S., Srinivasan, D., Member, S., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2015)CrossRefGoogle Scholar
  3. 3.
    Mhanna, S., Chapman, A.C., Verbic, G.: A fast distributed algorithm for large-scale demand response aggregation. IEEE Trans. Smart Grid 7(4), 2094–2107 (2016)CrossRefGoogle Scholar
  4. 4.
    Ogwumike, C., Short, M., Abugchem, F.: Heuristic optimization of consumer electricity costs using a generic cost model. Energies 9(1), 6 (2016)CrossRefGoogle Scholar
  5. 5.
    Mahmood, D., et al.: Realistic Scheduling Mechanism for smart homes. Energies 9(3), 1–28 (2016)CrossRefGoogle Scholar
  6. 6.
    Wei, Q., Lewis, F.L., Shi, G., Song, R.: Error-tolerant iterative adaptive dynamic programming for optimal renewable home energy scheduling and battery management. IEEE Trans. Ind. Electron. 64(12), 1–1 (2017)Google Scholar
  7. 7.
    Javaid, N., et al.: An intelligent load management system with renewable energy integration for smart homes. IEEE Access 5, 13587–13600 (2017)CrossRefGoogle Scholar
  8. 8.
    Javaid, N., et al.: Demand side management in nearly zero energy buildings using heuristic optimizations. Energies 10(8), 1131 (2017)CrossRefGoogle Scholar
  9. 9.
    Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energ. Build. 129, 452–470 (2016)CrossRefGoogle Scholar
  10. 10.
    Yang, X., Zhang, Y., Zhao, B., Huang, F., Chen, Y., Ren, S.: Optimal energy flow control strategy for a residential energy local network combined with demand-side management and real-time pricing. Energ. Build. 150, 177–188 (2017)CrossRefGoogle Scholar
  11. 11.
    Celik, B., Roche, R., Suryanarayanan, S., Bouquain, D., Miraoui, A.: Electric energy management in residential areas through coordination of multiple smart homes. Renew. Sustain. Energ. Rev. 80(May), 260–275 (2017)CrossRefGoogle Scholar
  12. 12.
    Koolen, D., Sadat-razavi, N., Ketter, W.: Applied sciences machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing (2017)CrossRefGoogle Scholar
  13. 13.
    Marzband, M., Ghazimirsaeid, S.S., Uppal, H., Fernando, T.: A real-time evaluation of energy management systems for smart hybrid home Microgrids. Electr. Power Syst. Res. 143, 624–633 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhou, Y.: The optimal home energy management strategy in smart grid. J. Renew. Sustain. Energ. 8, 45101 (2016)CrossRefGoogle Scholar
  15. 15.
    Keshtkar, A., Arzanpour, S.: An adaptive fuzzy logic system for residential energy management in smart grid environments. Appl. Energ. 186, 68–81 (2017)CrossRefGoogle Scholar
  16. 16.
    Lobaccaro, G., Carlucci, S., Löfström, E.: A review of systems and technologies for smart homes and smart grids. Energies 9(5), 1–33 (2016)CrossRefGoogle Scholar
  17. 17.
    Schulze, M., Heidenreich, S., Spieth, P.: The impact of energy management control systems on energy efficiency in the german manufacturing industry, vol. 0, no. 0, pp. 1–14 (2017)Google Scholar
  18. 18.
    Longe, O.M., Ouahada, K., Rimer, S., Ferreira, H.C., Han Vinck, A.J.: Distributed optimisation algorithm for demand side management in a grid-connected smart microgrid. Sustainability 9(7), 1088–1104 (2017)CrossRefGoogle Scholar
  19. 19.
    Wang, G.G., Deb, S., Coelho, L.D.S.: Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int. J. Bio Inspired Comput. 1(1), 1 (2015)CrossRefGoogle Scholar
  20. 20.
    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), 1–28 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Nadeem Javaid
    • 1
    Email author
  • Ihtisham Ullah
    • 1
  • Syed Shahab Zarin
    • 1
  • Mohsin Kamal
    • 1
  • Babatunji Omoniwa
    • 1
  • Abdul Mateen
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations