Advertisement

Simulating the Impacts of the Energy Consumption Using Multi-agent Systems

  • Fernanda P. Mota
  • Graçaliz Pereira Dimuro
  • Vagner Rosa
  • Silvia S. da C. Botelho
Part of the Communications in Computer and Information Science book series (CCIS, volume 365)

Abstract

Simulation of home use of electric energy is a very powerful tool for the purpose of studying, planning and managing at electric energy distribution companies. This paper presents the initial results obtained considering the paradigm of multiagent systems (namely, the NetLogo tool) for the of energy consumption simulation as a common resource. Distinct profiles of possible behaviors of consumers and household appliances with different powers are modeled and simulated using computational agents.

Keywords

Multiagent Systems NetLogo Electricity Consumption 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Multiner: Consumo de energia elétrica cresce no Brasil e Belo Monte é garantia para essa demanda (2012), http://www.multiner.com.br/multiner/Default.aspx?TabId=117
  2. 2.
    Aneel: Atlas de Energia Elétrica do Brasil (2012), http://www.aneel.gov.br/arquivos/pdf/livro_atlas.pdf
  3. 3.
    Russel, S., Norvig, P.: Artificial Intelligence: A modern approach, 2nd edn. Pearson Education (2003)Google Scholar
  4. 4.
    Sapkota, P.: Modeling Diffusion Using an Agent-Based Approach. PhD thesis, University of Toledo (2010)Google Scholar
  5. 5.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999), http://ccl.northwestern.edu/netlogo
  6. 6.
    Aneel: Aprenda a calcular o consumo de seu aparelho e economize energia (2011)Google Scholar
  7. 7.
    Braga, N.C.: O consumo da energia elétrica (EL015), Escrevendo sobre Tecnologia para as principais revistas do mundo desde 1966 (1966) Google Scholar
  8. 8.
    Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist. Open University Press (2005)Google Scholar
  9. 9.
    Van Dyke Parunak, H., Savit, R., Riolo, R.L.: Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. In: Sichman, J.S., Conte, R., Gilbert, N. (eds.) MABS 1998. LNCS (LNAI), vol. 1534, pp. 10–25. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Tisue, S., Wilensky, U.: Netlogo: A simple environment for modeling complexity. In: International Conference on Complex Systems, Boston (2004)Google Scholar
  11. 11.
    Le, X.H.B., Kashif, A., Ploix, S., Dugdale, J., Di Mascolo, M., Abras, S.: Simulating inhabitant behaviour to manage energy at home. In: International Building Performance Simulation Association Conference, Moret-sur-Loing, France (2010)Google Scholar
  12. 12.
    Klein, L., Kavulya, G., Jazizadeh, F., Kwak, J., Becerik-Gerber, B., Tambe, M.: Towards optimization of building energy and occupant comfort using multi-agent simulation. In: International Symposium on Automation and Robotics in Construction (2011)Google Scholar
  13. 13.
    Reinhart, C.: Lightswitch 2002: A model for manual control of electric lighting and blinds. Solar Energy 77, 15–28 (1994)CrossRefGoogle Scholar
  14. 14.
    Axelrod, R.: The complexity of cooperation: Agent-based models of competition and collaboration. Princeton Univ. Press, Princeton (1996)Google Scholar
  15. 15.
    Bermann, C.: Energia Para Quê E Para Quem No Brasil. Fase, Editora Livraria Física (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fernanda P. Mota
    • 2
  • Graçaliz Pereira Dimuro
    • 1
    • 2
  • Vagner Rosa
    • 2
  • Silvia S. da C. Botelho
    • 1
    • 2
  1. 1.Programa de Pós-Graduação em Modelagem ComputacionalUniversidade Federal do Rio Grande (FURG)Rio GrandeBrazil
  2. 2.Programa de Pós-Graduação em Engenharia de ComputaçãoUniversidade Federal do Rio Grande (FURG)Rio GrandeBrazil

Personalised recommendations