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Household Electric Load Pattern Consumption Enhanced Simulation by Random Behavior

  • Alabbas Alhaj AliEmail author
  • Doina Logofătu
  • Prachi Agrawal
  • Sreshtha Roy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

The demand for electricity is increasing exponentially and thus, the concern for energy conservation becomes important. The daily consumption of electricity by each family needs to be calculated which in turn would help to estimate the weekly, monthly and yearly electric consumption for a particular unit. The electricity consumed by each family depends upon various factors. The occupancy model of the family needs additionally to be considered. After studying the this model, one can predict at which hours of the day the load consumed is maximum and at which hours of the day it is minimum. Studying the load profiles of each family, the supplier of the electricity can estimate the consumption charge supply policy accordingly. While studying the load profile, we need to take into consideration various appliances and their demand behavior. In this paper, we summarize influential factors of house electrical consumption, the occupancy of the members of the house, and the electrical demand for lighting. It also explains various types of appliances usually employed in a house and their categorization based on behavior and how they contribute to the total load profile of a household.

Keywords

Electric load pattern Electric demand Software engineering Simulation Random behaviour 

References

  1. 1.
    Grandjean, A., Adnot, J., Binet, G.: A review and an analysis of the residential electric load curve models. Renew. Sustain. Energy Rev. 41, 6539–6565 (2012)CrossRefGoogle Scholar
  2. 2.
    Bartusch, C., Odlare, M., Wallin, F., Wester, L.: Exploring variance in residential electricity consumption: household features and building properties. Appl. Energy 92, 637–643 (2012). http://www.sciencedirect.com/science/article/pii/S030626191100256XCrossRefGoogle Scholar
  3. 3.
    Bedir, M., Hasselaar, E., Itard, L.: Determinants of electricity consumption in Dutch dwellings. Energy Build. 58, 194–207 (2013). http://www.sciencedirect.com/science/article/pii/S0378778812005257CrossRefGoogle Scholar
  4. 4.
    Chen, Y.T.: The factors affecting electricity consumption and the consumption characteristics in the residential sector-a case example of taiwan. Appl. Energy 9, 1484 (2017)Google Scholar
  5. 5.
    Cramer, J.C., et al.: Social and engineering determinants and their equity implications in residential electricity use. Energy 10(12), 1283–1291 (1985). http://www.sciencedirect.com/science/article/pii/0360544285901392CrossRefGoogle Scholar
  6. 6.
    Grandjean, A., Binet, G., Bieret, J., Adnot, J., Duplessis, B.: A functional analysis of electrical load curve modelling for some households specific electricity end-uses. Appl. Energy hal-00770135, 24 p. (2011). https://hal-mines-paristech.archives-ouvertes.fr/hal-00770135
  7. 7.
    Hobby, J.D., Tucci, G.H.: Analysis of the residential, commercial and industrial electricity consumption. In: 2011 IEEE PES Innovative Smart Grid Technologies, pp. 1–7. IEEE, November 2011Google Scholar
  8. 8.
    Huebner, G., Shipworth, D., Hamilton, I., Chalabi, Z., Oreszczyn, T.: Understanding electricity consumption: a comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes. Appl. Energy 177, 692–702 (2016). http://www.sciencedirect.com/science/article/pii/S0306261916305360CrossRefGoogle Scholar
  9. 9.
    Jones, H.B., Lee, S.R.: Factors influencing energy consumption and costs in broiler processing plants in the south. J. Agric. Appl. Econ. 10(2), 63–68 (1978).  https://doi.org/10.1017/S0081305200014382CrossRefGoogle Scholar
  10. 10.
    Ndiaye, D., Gabriel, K.: Principal component analysis of the electricity consumption in residential dwellings. Energy Build. 43(2), 446–453 (2011). http://www.sciencedirect.com/science/article/pii/S0378778810003592CrossRefGoogle Scholar
  11. 11.
    Richardson, I., Thomson, M., Infield, D., Delahunty, A.: Domestic lighting: a high-resolution energy demand model. Appl. Energy 41, 781–789 (2009)Google Scholar
  12. 12.
    Kalogirou, S.A., Bojic, M.: Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 41, 479–491 (2000).  https://doi.org/10.1016/S0360-5442(99)00086-9CrossRefGoogle Scholar
  13. 13.
    Wiesmann, D., Azevedo, I.L., Ferrão, P., Fernández, J.E.: Residential electricity consumption in Portugal: findings from top-down and bottom-up models. Energy Policy 39(5), 2772–2779 (2011). http://www.sciencedirect.com/science/article/pii/S030142151100139XCrossRefGoogle Scholar
  14. 14.
    Wilson, C., Hargreaves, T., Hauxwell-Baldwin, R.: Smart homes and their users: a systematic analysis and key challenges. Pers. Ubiquit. Comput. 19(2), 463–476 (2015).  https://doi.org/10.1007/s00779-014-0813-0CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alabbas Alhaj Ali
    • 1
    Email author
  • Doina Logofătu
    • 1
  • Prachi Agrawal
    • 1
  • Sreshtha Roy
    • 1
  1. 1.Department of Computer Science and EngineeringFrankfurt University of Applied SciencesFrankfurtGermany

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