Abstract
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.
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Acknowledgments
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.
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Borunda, M., Nicholson, A.E., Garduno, R., Sadafi, H. (2018). On the Modelling of the Energy System of a Country for Decision Making Using Bayesian Artificial Intelligence – A Case Study for Mexico. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_3
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