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User Satisfaction Based Home Energy Management System for Smart Cities

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 612))

Abstract

With the advent of smart grid and demand side management techniques, users have opportunity to reduce their electricity cost without compromising their comfort much. In this paper, we evaluate the performance of home energy management system based on user satisfaction. Our objective is to maximize the total user satisfaction within user defined budget. For budget three different scenarios are presented that are; $0.25/day, $0.50/day and $1.00/day. To obtain the desired satisfaction three optimization techniques are used: genetic algorithm (GA), enhanced differential evolution (EDE) algorithm, harmony search algorithm (HSA) and their results are compared in terms of achieved satisfaction.

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References

  1. Lo, C.H., Ansari, N.: The progressive smart grid system from both power and communications aspects. IEEE Commun. Surv. Tutorials 14(3), 799–821 (2012)

    Google Scholar 

  2. Tiwari, N., Srivastava, L.: Generation scheduling and micro-grid energy management using differential evolution algorithm. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–7. IEEE, March 2016

    Google Scholar 

  3. Zhang, J., Wu, Y., Guo, Y., Wang, B., Wang, H., Liu, H.: A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl. Energy 183, 791–804 (2016)

    Article  Google Scholar 

  4. Ahmad, A., Javaid, N., Guizani, M., Alrajeh, N. and Khan, Z.A.: An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Trans. Indus. Inform. (2016)

    Google Scholar 

  5. Moradi, M.H., Abedini, M., Tousi, S.R., Hosseinian, S.M.: Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm. Int. J. Electr. Power Energy Syst. 73, 1015–1024 (2015)

    Article  Google Scholar 

  6. Trujillo, L.: Demand-side management: optimising through differential evolution plug-in electric vehicles to partially fulfil load demand. In: Computational Intelligence: International Joint Conference, IJCCI 2015, Lisbon, Portugal, 12–14 November 2015, Revised Selected Papers, vol. 669, p. 155. Springer, Cham, December 2016

    Google Scholar 

  7. Carreiro, A.M., Oliveira, C., Antunes, C.H. Jorge, H.M.: An energy management system aggregator based on an integrated evolutionary and differential evolution approach. In: European Conference on the Applications of Evolutionary Computation, pp. 252–264. Springer, Cham, April 2015

    Google Scholar 

  8. Arafa, M., Sallam, E.A. Fahmy, M.M.: An enhanced differential evolution optimization algorithm. In: 2014 Fourth International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 216–225. IEEE, May 2014

    Google Scholar 

  9. Karaboğa, D., Ökdem, S.: A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turk. J. Electr. Eng. Comput. Sci. 12(1), 53–60 (2004)

    Google Scholar 

  10. Muralitharan, K., Sakthivel, R., Shi, Y.: Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177, 110–119 (2016)

    Article  Google Scholar 

  11. Muratori, M., Rizzoni, G.: Residential demand response: dynamic energy management and time-varying electricity pricing. IEEE Trans. Power Syst. 31(2), 1108–1117 (2016)

    Article  Google Scholar 

  12. Rastegar, M., Fotuhi-Firuzabad, M., Zareipour, H.: Home energy management incorporating operational priority of appliances. Int. J. Electr. Power Energy Syst. 74, 286–292 (2016)

    Article  Google Scholar 

  13. Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wirel. Pers. Commun. 93(2), 481–502 (2017)

    Article  Google Scholar 

  14. Shakeri, M., Shayestegan, M., Abunima, H., Reza, S.S., Akhtaruzzaman, M., Alamoud, A.R.M., Sopian, K., Amin, N.: An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy Build. 138, 154–164 (2017)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Ahmed, N., Levorato, M., Li, G.P.: Residential consumer-centric demand side management. IEEE Trans. Smart Grid (2017)

    Google Scholar 

  17. Di Piazza, M.C., La Tona, G., Luna, M., Di Piazza, A.: A two-stage energy management system for smart buildings reducing the impact of demand uncertainty. Energy Build. 139, 1–9 (2017)

    Article  Google Scholar 

  18. Yuce, B., Rezgui, Y., Mourshed, M.: ANN–GA smart appliance scheduling for optimised energy management in the domestic sector. Energy Build. 111, 311–325 (2016)

    Article  Google Scholar 

  19. Ogunjuyigbe, A.S.O., Ayodele, T.R., Akinola, O.A.: User satisfaction-induced demand side load management in residential buildings with user budget constraint. Appl. Energy 187, 352–366 (2017)

    Article  Google Scholar 

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Correspondence to Nadeem Javaid .

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Feroze, F. et al. (2018). User Satisfaction Based Home Energy Management System for Smart Cities. In: Barolli, L., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2017. Advances in Intelligent Systems and Computing, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-61542-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-61542-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61541-7

  • Online ISBN: 978-3-319-61542-4

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