On the Modelling of the Energy System of a Country for Decision Making Using Bayesian Artificial Intelligence – A Case Study for Mexico

  • Monica Borunda
  • Ann E. Nicholson
  • Raul Garduno
  • Hoss Sadafi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)


Energy efficiency has attracted the attention of many governments around the world due to the urgent call to reduce investments in energy infrastructure, lower fossil fuel dependency, integrate renewable energies, improve consumer welfare and reduce CO2 emissions. The conservative and smart use of energy is one of the main approaches to improve energy efficiency. However, the management of energy at the national level is a complex decision making problem involving uncertainty and therefore, Bayesian Networks are suitable paradigm to deal with this task. In this work, we present a progress report on the development of a decision making method, based on Bayesian decision networks, for the efficient use of energy as a function of the cost, efficiency and CO2 emissions from the source of energy used.


Energy efficiency Decision making Bayesian networks Smart energy system 



Monica Borunda wishes to thank Consejo Nacional de Ciencia y Tecnología, CONACYT, support for her Catedra Research Position with ID 71557, and to Instituto Nacional de Electricidad y Energías Limpias, INEEL, for its hospitality. She also wants to thank Australia-APEC woman in research fellowship for its grant to perform this research.


  1. 1.
    InterAcademy Council: Lighting the Way: Toward a Sustainable Energy Future, p. xvii (2007)Google Scholar
  2. 2. The Twin Pillars of Sustainable Energy: Synergies between Energy Efficiency and Renewable Energy Technology and Policy, 11 January 2015Google Scholar
  3. 3.
  4. 4.
    Kapusuzoglu, A., Karan, M.B.: The drivers of energy consumption in developing countries. In: Dorsman, A., Simpson, J., Westerman, W. (eds.) Energy Economics and Financial Markets, pp. 49–69. Springer, Heidelberg (2013). Scholar
  5. 5.
    Ngo, C., Natowitz, J.: Our Energy Future: Resources, Alternatives, and the Environment, 2nd edn (2016)CrossRefGoogle Scholar
  6. 6.
    Yildirim, H.H.: Economic growth and energy consumption for OECD countries. In: Bilgin, M.H., Danis, H., Demir, E., Can, U. (eds.) Regional Studies on Economic Growth, Financial Economics and Management. ESBE, vol. 7, pp. 245–255. Springer, Cham (2017). Scholar
  7. 7.
    Ozturk, F.: Energy consumption – GDP causality in Middle East and North Africa (MENA) countries. Energy Sources Part B Econ. Plann. Policy 12(3), 231–236 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Al-mulali, U., Sab, C.N.C.: Energy consumption, CO2 emissions, and development in the UAE. Energy Sources Part B Econ. Plann. Policy 13(4), 1–6 (2018)Google Scholar
  9. 9.
    Bakirtas, T., Akpolat, A.G.: The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries. Energy 147, 110–121 (2019)CrossRefGoogle Scholar
  10. 10.
    Lee, S.-J., Yoo, S.H.: Energy consumption, CO2 emission, and economic growth: evidence from Mexico. Energy Sources Part B Econ. Plann. Policy 11(8), 711–717 (2016)CrossRefGoogle Scholar
  11. 11.
    Gómez, M., Ciarreta, A., Zarraga, A.: Linear and nonlinear causality between energy consumption and economic growth: the case of Mexico 1965–2014. Energies 11(4), 784 (2018)CrossRefGoogle Scholar
  12. 12.
    Omer, A.M.: Energy, environment and sustainable development. Renew. Sustain. Energy Rev. 12(9), 2265–2300 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lentz, A.E., Angeles, M., Glenn, E., Ramírez, N., González, J.E.: On the recent climatological and energy trends in Mexico City. In: ASME 2016 10th International Conference on Energy Systainability collocated with the ASME 2016 Power Conference and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology (2016)Google Scholar
  14. 14.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)zbMATHGoogle Scholar
  15. 15.
    Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence, 2nd edn. CRC Press, Boca Raton (2011)zbMATHGoogle Scholar
  16. 16.
    Howard, R., Matheson, J.: Influence diagrams. In: Howard, R., Matheson, J. (eds.) Readings in Decision Analysis, pp. 763–771. Strategic Decisions Group, Menlo Park (1981)Google Scholar
  17. 17.
    Statistical Review of World Energy. Workbook, London (2016)Google Scholar
  18. 18.
    International Energy Statistics. Energy Information Administration. Accessed 5 June 2013Google Scholar
  19. 19.
    Top 5 reasons to be energy efficient. Alliance to Save Energy (ASE). Accessed 14 June 2016Google Scholar
  20. 20.
  21. 21.
  22. 22.
    Garduno, R., Ibarguengoitia, P.: On the development of industrial-grade intelligent supervisory systems for power plant operation. In: IEEE Power Engineering Society General Meeting, Canada (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Monica Borunda
    • 1
  • Ann E. Nicholson
    • 2
  • Raul Garduno
    • 3
  • Hoss Sadafi
    • 4
  1. 1.Conacyt - Instituto Nacional de Electricidad y Energías LimpiasCuernavacaMexico
  2. 2.Faculty of Information TechnologyMonash UniversityMelbourneAustralia
  3. 3.Instituto Nacional de Electricidad y Energías LimpiasCuernavacaMexico
  4. 4.Department of Mechanical and Aerospace EngineeringMonash UniversityMelbourneAustralia

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