Potential roles of artificial intelligence in the lci of renewable energy systems

Paul A. Adedeji; Stephen A. Akinlabi; Nkosinathi Madushele; Obafemi O. Olatunji

doi:10.1007/978-981-15-5753-8_26

Potential roles of artificial intelligence in the lci of renewable energy systems

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

4 Citations (Scopus)

Abstract

Energy revolution from the conventional fossil fuels to clean energy is fast gaining traction with renewable and clean energy sources blazing the trail on the global scale. This has consequentially reduced electricity prices in certain countries and reduced carbon footprints in both manufacturing and service industries. Asides the advantages of these clean energy technologies, the assessment of their life cycle has recently gained more attention with life cycle inventory playing a major role. Life cycle inventory is a critical component in life cycle assessment. However, a life cycle inventory study is as accurate as the data used. This study presents a roadmap to the use of artificial intelligence (AI) techniques in life cycle inventory (LCI). The data chain for efficient local resident data availability for LCA studies was considered with a focus on AI integration. In addition, a framework for the use of AI in LCI was developed. The study concluded that it was possible to proffer solution to LCI data unavailability problem using AI with the joint support of public and private partners.

Original language	English
Title of host publication	Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019
Editors	Seyed Sattar Emamian, Farazila Yusof, Mokhtar Awang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	275-285
Number of pages	11
ISBN (Print)	9789811557521
DOIs	https://doi.org/10.1007/978-981-15-5753-8_26
Publication status	Published - 2020
Event	5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019 - Kuala Lumpur, Malaysia Duration: 19 Nov 2019 → 21 Nov 2019

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019
Country/Territory	Malaysia
City	Kuala Lumpur
Period	19/11/19 → 21/11/19

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action

Keywords

Artificial intelligence
Energy systems
Life cycle assessment
Life cycle inventory
Renewable energy sources

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

Access to Document

10.1007/978-981-15-5753-8_26

Cite this

Adedeji, P. A., Akinlabi, S. A., Madushele, N., & Olatunji, O. O. (2020). Potential roles of artificial intelligence in the lci of renewable energy systems. In S. S. Emamian, F. Yusof, & M. Awang (Eds.), Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019 (pp. 275-285). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-5753-8_26

Adedeji, Paul A. ; Akinlabi, Stephen A. ; Madushele, Nkosinathi et al. / Potential roles of artificial intelligence in the lci of renewable energy systems. Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. editor / Seyed Sattar Emamian ; Farazila Yusof ; Mokhtar Awang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 275-285 (Lecture Notes in Mechanical Engineering).

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abstract = "Energy revolution from the conventional fossil fuels to clean energy is fast gaining traction with renewable and clean energy sources blazing the trail on the global scale. This has consequentially reduced electricity prices in certain countries and reduced carbon footprints in both manufacturing and service industries. Asides the advantages of these clean energy technologies, the assessment of their life cycle has recently gained more attention with life cycle inventory playing a major role. Life cycle inventory is a critical component in life cycle assessment. However, a life cycle inventory study is as accurate as the data used. This study presents a roadmap to the use of artificial intelligence (AI) techniques in life cycle inventory (LCI). The data chain for efficient local resident data availability for LCA studies was considered with a focus on AI integration. In addition, a framework for the use of AI in LCI was developed. The study concluded that it was possible to proffer solution to LCI data unavailability problem using AI with the joint support of public and private partners.",

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author = "Adedeji, \{Paul A.\} and Akinlabi, \{Stephen A.\} and Nkosinathi Madushele and Olatunji, \{Obafemi O.\}",

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Adedeji, PA , Akinlabi, SA , Madushele, N & Olatunji, OO 2020, Potential roles of artificial intelligence in the lci of renewable energy systems. in SS Emamian, F Yusof & M Awang (eds), Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 275-285, 5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019, Kuala Lumpur, Malaysia, 19/11/19. https://doi.org/10.1007/978-981-15-5753-8_26

Potential roles of artificial intelligence in the lci of renewable energy systems. / Adedeji, Paul A.; Akinlabi, Stephen A.; Madushele, Nkosinathi et al.
Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. ed. / Seyed Sattar Emamian; Farazila Yusof; Mokhtar Awang. Springer Science and Business Media Deutschland GmbH, 2020. p. 275-285 (Lecture Notes in Mechanical Engineering).

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

TY - GEN

T1 - Potential roles of artificial intelligence in the lci of renewable energy systems

AU - Adedeji, Paul A.

AU - Akinlabi, Stephen A.

AU - Madushele, Nkosinathi

AU - Olatunji, Obafemi O.

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2020.

PY - 2020

Y1 - 2020

N2 - Energy revolution from the conventional fossil fuels to clean energy is fast gaining traction with renewable and clean energy sources blazing the trail on the global scale. This has consequentially reduced electricity prices in certain countries and reduced carbon footprints in both manufacturing and service industries. Asides the advantages of these clean energy technologies, the assessment of their life cycle has recently gained more attention with life cycle inventory playing a major role. Life cycle inventory is a critical component in life cycle assessment. However, a life cycle inventory study is as accurate as the data used. This study presents a roadmap to the use of artificial intelligence (AI) techniques in life cycle inventory (LCI). The data chain for efficient local resident data availability for LCA studies was considered with a focus on AI integration. In addition, a framework for the use of AI in LCI was developed. The study concluded that it was possible to proffer solution to LCI data unavailability problem using AI with the joint support of public and private partners.

AB - Energy revolution from the conventional fossil fuels to clean energy is fast gaining traction with renewable and clean energy sources blazing the trail on the global scale. This has consequentially reduced electricity prices in certain countries and reduced carbon footprints in both manufacturing and service industries. Asides the advantages of these clean energy technologies, the assessment of their life cycle has recently gained more attention with life cycle inventory playing a major role. Life cycle inventory is a critical component in life cycle assessment. However, a life cycle inventory study is as accurate as the data used. This study presents a roadmap to the use of artificial intelligence (AI) techniques in life cycle inventory (LCI). The data chain for efficient local resident data availability for LCA studies was considered with a focus on AI integration. In addition, a framework for the use of AI in LCI was developed. The study concluded that it was possible to proffer solution to LCI data unavailability problem using AI with the joint support of public and private partners.

KW - Artificial intelligence

KW - Energy systems

KW - Life cycle assessment

KW - Life cycle inventory

KW - Renewable energy sources

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M3 - Conference contribution

AN - SCOPUS:85091313888

SN - 9789811557521

T3 - Lecture Notes in Mechanical Engineering

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BT - Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019

A2 - Emamian, Seyed Sattar

A2 - Yusof, Farazila

A2 - Awang, Mokhtar

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019

Y2 - 19 November 2019 through 21 November 2019

ER -

Adedeji PA , Akinlabi SA , Madushele N, Olatunji OO. Potential roles of artificial intelligence in the lci of renewable energy systems. In Emamian SS, Yusof F, Awang M, editors, Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019. Springer Science and Business Media Deutschland GmbH. 2020. p. 275-285. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-981-15-5753-8_26

Potential roles of artificial intelligence in the lci of renewable energy systems

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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