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

doi:10.1007/978-3-030-04497-8_3

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

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 CO ₂ 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 CO ₂ emissions from the source of energy used.

Original language	English
Title of host publication	Advances in Computational Intelligence
Subtitle of host publication	17th Mexican International Conference on Artificial Intelligence, MICAI 2018 Guadalajara, Mexico, October 22–27, 2018 Proceedings, Part II
Editors	Ildar Batyrshin, María de Lourdes Martínez-Villaseñor, Hiram Eredín Ponce Espinosa
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	30-46
Number of pages	17
ISBN (Electronic)	9783030044978
ISBN (Print)	9783030044961
DOIs	https://doi.org/10.1007/978-3-030-04497-8_3
Publication status	Published - 2018
Event	Mexican International Conference on Artificial Intelligence 2018 - Guadalajara, Mexico Duration: 22 Oct 2018 → 27 Oct 2018 Conference number: 17th http://www.micai.org/2018/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11289
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Mexican International Conference on Artificial Intelligence 2018
Abbreviated title	MICAI 2018
Country/Territory	Mexico
City	Guadalajara
Period	22/10/18 → 27/10/18
Internet address	http://www.micai.org/2018/

Keywords

Bayesian networks
Decision making
Energy efficiency
Smart energy system

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-04497-8_3

Cite this

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 I. Batyrshin, M. de Lourdes Martínez-Villaseñor, & H. E. Ponce Espinosa (Eds.), Advances in Computational Intelligence: 17th Mexican International Conference on Artificial Intelligence, MICAI 2018 Guadalajara, Mexico, October 22–27, 2018 Proceedings, Part II (pp. 30-46). (Lecture Notes in Computer Science ; Vol. 11289 ). Springer. https://doi.org/10.1007/978-3-030-04497-8_3

Borunda, Monica ; Nicholson, Ann E. ; Garduno, Raul et al. / On the modelling of the energy system of a country for decision making using bayesian artificial intelligence – a case study for Mexico. Advances in Computational Intelligence: 17th Mexican International Conference on Artificial Intelligence, MICAI 2018 Guadalajara, Mexico, October 22–27, 2018 Proceedings, Part II. editor / Ildar Batyrshin ; María de Lourdes Martínez-Villaseñor ; Hiram Eredín Ponce Espinosa. Cham Switzerland : Springer, 2018. pp. 30-46 (Lecture Notes in Computer Science ).

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title = "On the modelling of the energy system of a country for decision making using bayesian artificial intelligence – a case study for Mexico",

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 CO 2 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 CO 2 emissions from the source of energy used. ",

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Borunda, M, Nicholson, AE, 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 I Batyrshin, M de Lourdes Martínez-Villaseñor & HE Ponce Espinosa (eds), Advances in Computational Intelligence: 17th Mexican International Conference on Artificial Intelligence, MICAI 2018 Guadalajara, Mexico, October 22–27, 2018 Proceedings, Part II. Lecture Notes in Computer Science , vol. 11289 , Springer, Cham Switzerland, pp. 30-46, Mexican International Conference on Artificial Intelligence 2018, Guadalajara, Mexico, 22/10/18. https://doi.org/10.1007/978-3-030-04497-8_3

On the modelling of the energy system of a country for decision making using bayesian artificial intelligence – a case study for Mexico. / Borunda, Monica; Nicholson, Ann E.; Garduno, Raul et al.
Advances in Computational Intelligence: 17th Mexican International Conference on Artificial Intelligence, MICAI 2018 Guadalajara, Mexico, October 22–27, 2018 Proceedings, Part II. ed. / Ildar Batyrshin; María de Lourdes Martínez-Villaseñor; Hiram Eredín Ponce Espinosa. Cham Switzerland: Springer, 2018. p. 30-46 (Lecture Notes in Computer Science ; Vol. 11289 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - On the modelling of the energy system of a country for decision making using bayesian artificial intelligence – a case study for Mexico

AU - Borunda, Monica

AU - Nicholson, Ann E.

AU - Garduno, Raul

AU - Sadafi, Hoss

N1 - Conference code: 17th

PY - 2018

Y1 - 2018

N2 - 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 CO 2 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 CO 2 emissions from the source of energy used.

AB - 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 CO 2 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 CO 2 emissions from the source of energy used.

KW - Bayesian networks

KW - Decision making

KW - Energy efficiency

KW - Smart energy system

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AN - SCOPUS:85059965592

SN - 9783030044961

T3 - Lecture Notes in Computer Science

SP - 30

EP - 46

BT - Advances in Computational Intelligence

A2 - Batyrshin, Ildar

A2 - de Lourdes Martínez-Villaseñor, María

A2 - Ponce Espinosa, Hiram Eredín

PB - Springer

CY - Cham Switzerland

T2 - Mexican International Conference on Artificial Intelligence 2018

Y2 - 22 October 2018 through 27 October 2018

ER -

Borunda M, Nicholson AE, Garduno R, Sadafi H. 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, de Lourdes Martínez-Villaseñor M, Ponce Espinosa HE, editors, Advances in Computational Intelligence: 17th Mexican International Conference on Artificial Intelligence, MICAI 2018 Guadalajara, Mexico, October 22–27, 2018 Proceedings, Part II. Cham Switzerland: Springer. 2018. p. 30-46. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-04497-8_3

On the modelling of the energy system of a country for decision making using bayesian artificial intelligence – a case study for Mexico

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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