Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks

Monica Borunda; Raul Garduno; Ann E. Nicholson; Javier de la Cruz

doi:10.1007/978-3-030-33749-0_40

Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks

Monica Borunda, Raul Garduno, Ann E. Nicholson, Javier de la Cruz

Department of Data Science & AI

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

1 Citation (Scopus)

Abstract

Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO₂ emissions and the achieved savings.

Original language	English
Title of host publication	Advances in Soft Computing
Subtitle of host publication	18th Mexican International Conference on Artificial Intelligence, MICAI 2019 Xalapa, Mexico, October 27 – November 2, 2019 Proceedings
Editors	Lourdes Martínez-Villaseñor, Ildar Batyrshin, Antonio Marín-Hernández
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	493-506
Number of pages	14
ISBN (Electronic)	9783030337490
ISBN (Print)	9783030337483
DOIs	https://doi.org/10.1007/978-3-030-33749-0_40
Publication status	Published - 2019
Event	Mexican International Conference on Artificial Intelligence 2019 - Xalapa, Mexico Duration: 28 Oct 2019 → 1 Nov 2019 Conference number: 18th http://www.micai.org/2019/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11835
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Mexican International Conference on Artificial Intelligence 2019
Abbreviated title	MICAI 2019
Country/Territory	Mexico
City	Xalapa
Period	28/10/19 → 1/11/19
Internet address	http://www.micai.org/2019/

Keywords

Bayesian Networks
Decision-making
Energy demand
Small-scale wind turbine technology
Wind energy

UN SDGs

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

Access to Document

10.1007/978-3-030-33749-0_40

Cite this

Borunda, M., Garduno, R., Nicholson, A. E., & de la Cruz, J. (2019). Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks. In L. Martínez-Villaseñor, I. Batyrshin, & A. Marín-Hernández (Eds.), Advances in Soft Computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019 Xalapa, Mexico, October 27 – November 2, 2019 Proceedings (pp. 493-506). (Lecture Notes in Computer Science; Vol. 11835 ). Springer. https://doi.org/10.1007/978-3-030-33749-0_40

Borunda, Monica ; Garduno, Raul ; Nicholson, Ann E. et al. / Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks. Advances in Soft Computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019 Xalapa, Mexico, October 27 – November 2, 2019 Proceedings. editor / Lourdes Martínez-Villaseñor ; Ildar Batyrshin ; Antonio Marín-Hernández. Cham Switzerland : Springer, 2019. pp. 493-506 (Lecture Notes in Computer Science).

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title = "Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks",

abstract = "Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO2 emissions and the achieved savings.",

keywords = "Bayesian Networks, Decision-making, Energy demand, Small-scale wind turbine technology, Wind energy",

author = "Monica Borunda and Raul Garduno and Nicholson, {Ann E.} and {de la Cruz}, Javier",

year = "2019",

doi = "10.1007/978-3-030-33749-0_40",

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series = "Lecture Notes in Computer Science",

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Borunda, M, Garduno, R, Nicholson, AE & de la Cruz, J 2019, Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks. in L Martínez-Villaseñor, I Batyrshin & A Marín-Hernández (eds), Advances in Soft Computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019 Xalapa, Mexico, October 27 – November 2, 2019 Proceedings. Lecture Notes in Computer Science, vol. 11835 , Springer, Cham Switzerland, pp. 493-506, Mexican International Conference on Artificial Intelligence 2019, Xalapa, Mexico, 28/10/19. https://doi.org/10.1007/978-3-030-33749-0_40

Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks. / Borunda, Monica; Garduno, Raul; Nicholson, Ann E. et al.
Advances in Soft Computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019 Xalapa, Mexico, October 27 – November 2, 2019 Proceedings. ed. / Lourdes Martínez-Villaseñor; Ildar Batyrshin; Antonio Marín-Hernández. Cham Switzerland: Springer, 2019. p. 493-506 (Lecture Notes in Computer Science; Vol. 11835 ).

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

TY - GEN

T1 - Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks

AU - Borunda, Monica

AU - Garduno, Raul

AU - Nicholson, Ann E.

AU - de la Cruz, Javier

N1 - Conference code: 18th

PY - 2019

Y1 - 2019

N2 - Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO2 emissions and the achieved savings.

AB - Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO2 emissions and the achieved savings.

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KW - Decision-making

KW - Energy demand

KW - Small-scale wind turbine technology

KW - Wind energy

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DO - 10.1007/978-3-030-33749-0_40

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T3 - Lecture Notes in Computer Science

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EP - 506

BT - Advances in Soft Computing

A2 - Martínez-Villaseñor, Lourdes

A2 - Batyrshin, Ildar

A2 - Marín-Hernández, Antonio

PB - Springer

CY - Cham Switzerland

T2 - Mexican International Conference on Artificial Intelligence 2019

Y2 - 28 October 2019 through 1 November 2019

ER -

Borunda M, Garduno R, Nicholson AE, de la Cruz J. Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks. In Martínez-Villaseñor L, Batyrshin I, Marín-Hernández A, editors, Advances in Soft Computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019 Xalapa, Mexico, October 27 – November 2, 2019 Proceedings. Cham Switzerland: Springer. 2019. p. 493-506. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-33749-0_40

Assessment of small-scale wind turbines to meet high-energy demand in Mexico with Bayesian Decision Networks

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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