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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Sustainability Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2977-3504/ada77b |
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| Summary: | 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. |
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| ISSN: | 2977-3504 |