Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment
The increasing demand for sustainable energy systems (SES) has driven significant advancements in the fields of techno-economic analysis (TEA) and life cycle assessment (LCA). This comprehensive review explores the integration of machine learning (ML) techniques into these assessments to address inh...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Sustainability Science and Technology |
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| Online Access: | https://doi.org/10.1088/2977-3504/ada77b |
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| author | Ebrahimpourboura Zahra Mosalpuri Manish Jonas Baltrusaitis Dubey Pallavi Mba Wright Mark |
| author_facet | Ebrahimpourboura Zahra Mosalpuri Manish Jonas Baltrusaitis Dubey Pallavi Mba Wright Mark |
| author_sort | Ebrahimpourboura Zahra |
| collection | DOAJ |
| description | The increasing demand for sustainable energy systems (SES) has driven significant advancements in the fields of techno-economic analysis (TEA) and life cycle assessment (LCA). This comprehensive review explores the integration of machine learning (ML) techniques into these assessments to address inherent data limitations and uncertainties. TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. The review’s scope includes recent TEA and LCA of SES to understand gaps in current practices, and ML SES studies that address these practices. Our literature search identified only three papers integrating TEA, LCA, and ML. Many studies investigate combinations of TEA or LCA with ML. However, there are unique challenges and opportunities for considering all three aspects of SES. Thus, we propose near- and long-term opportunities to further integrate ML with TEA and LCA. Key case studies demonstrate the transformative potential of ML in improving economic viability and environmental sustainability, highlighting its role in predicting system performance, optimizing configurations, and reducing costs and impacts. The review identifies critical areas for future research, including improving data quality, advancing ML techniques, interdisciplinary training, real-world applications, and policy considerations. This integration represents a significant advancement in the field, offering new opportunities for innovation and optimization in sustainable energy technology assessments. |
| format | Article |
| id | doaj-art-e5ebc942e9fd4eb5b71b3e5c238ad39d |
| institution | DOAJ |
| issn | 2977-3504 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Sustainability Science and Technology |
| spelling | doaj-art-e5ebc942e9fd4eb5b71b3e5c238ad39d2025-08-20T03:06:57ZengIOP PublishingSustainability Science and Technology2977-35042025-01-012101200110.1088/2977-3504/ada77bEmerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessmentEbrahimpourboura Zahra0https://orcid.org/0000-0002-1117-9902Mosalpuri Manish1Jonas Baltrusaitis2https://orcid.org/0000-0001-5634-955XDubey Pallavi3Mba Wright Mark4https://orcid.org/0000-0003-1468-2391Department of Mechanical Engineering, Iowa State University , Ames, IA 50011, United States of AmericaDepartment of Mechanical Engineering, Iowa State University , Ames, IA 50011, United States of AmericaChemical & Biomolecular Engineering, Lehigh University , Bethlehem, PA 18015, United States of AmericaDepartment of Mechanical Engineering, Iowa State University , Ames, IA 50011, United States of America; Bioeconomy Institute , Iowa State University, Ames, IA 50011, United States of AmericaDepartment of Mechanical Engineering, Iowa State University , Ames, IA 50011, United States of America; Bioeconomy Institute , Iowa State University, Ames, IA 50011, United States of AmericaThe increasing demand for sustainable energy systems (SES) has driven significant advancements in the fields of techno-economic analysis (TEA) and life cycle assessment (LCA). This comprehensive review explores the integration of machine learning (ML) techniques into these assessments to address inherent data limitations and uncertainties. TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. The review’s scope includes recent TEA and LCA of SES to understand gaps in current practices, and ML SES studies that address these practices. Our literature search identified only three papers integrating TEA, LCA, and ML. Many studies investigate combinations of TEA or LCA with ML. However, there are unique challenges and opportunities for considering all three aspects of SES. Thus, we propose near- and long-term opportunities to further integrate ML with TEA and LCA. Key case studies demonstrate the transformative potential of ML in improving economic viability and environmental sustainability, highlighting its role in predicting system performance, optimizing configurations, and reducing costs and impacts. The review identifies critical areas for future research, including improving data quality, advancing ML techniques, interdisciplinary training, real-world applications, and policy considerations. This integration represents a significant advancement in the field, offering new opportunities for innovation and optimization in sustainable energy technology assessments.https://doi.org/10.1088/2977-3504/ada77bsustainable energy systemstechno-economic analysislife cycle assessmentmachine learning |
| spellingShingle | Ebrahimpourboura Zahra Mosalpuri Manish Jonas Baltrusaitis Dubey Pallavi Mba Wright Mark Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment Sustainability Science and Technology sustainable energy systems techno-economic analysis life cycle assessment machine learning |
| title | Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment |
| title_full | Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment |
| title_fullStr | Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment |
| title_full_unstemmed | Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment |
| title_short | Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment |
| title_sort | emerging trends in sustainable energy system assessments integration of machine learning with techno economic analysis and lifecycle assessment |
| topic | sustainable energy systems techno-economic analysis life cycle assessment machine learning |
| url | https://doi.org/10.1088/2977-3504/ada77b |
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