The DOI matches its Crossref record; 172 of the 174 references it cites were found in a registry or an index.
Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions
Abstract
The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2°C, has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems.
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“However, this involves high modeling complexity due to diverse constraints and dynamics [35], overcoming data fragmentation from historically siloed operations ( [23]; linking to policy/governance, Section 3.2.3),addressing interoperability deficits [33], and navigating regulatory/market designs often ill-suited for cross-sector optimization [38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [33]
Citations
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Machine learning applications in energy systems: current trends, challenges, and research directions
Aslam, S., Aung, P. P., Rafsanjani, A. S., & Majeed, A. P. P. A. (2025) · Energy Informatics · doi:10.1186/s42162-025-00524-6 · Cited by 112
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Rasheed, M. Q., Yuhuan, Z., Nazir, M., Ahmed, Z., & Yu, X. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102761 · Cited by 67
Noura, H., Allal, Z., Salman, O., & Chahine, K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109503 · Cited by 63
Self-citationA review on machine learning applications in hydrogen energy systems
Allal, Z., Noura, H., Salman, O., Vernier, F., & Chahine, K. (2025) · International Journal of Thermofluids · doi:10.1016/j.ijft.2025.101119 · Cited by 50
Self-citation
Metadata
- Authors
- Allal, Z., Noura, H. N., Salman, O., & Chahine, K.
- Affiliations
- Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies (FR)
- American University of Beirut (LB)
- American University of the Middle East (KW)
- Corresponding author
- Chahine, Khaled
- Venue
- Journal of Environmental Management
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 354
- Pages
- 120392
- ISSN
- 0301-4797 (ISSN-L)
- OpenAlex
- W4392091061
- Cited by
- 130
- References
- 174
- Citations by year
- 2026: 48 · 2025: 62 · 2024: 20
- FWCI
- 13.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Environmental Management (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-17-EURE-0002)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
- Keywords
- renewable energy systems, machine learning, renewable energy forecasting, Paris Agreement, sustainable energy, renewable energy technologies
- MeSH terms
- Machine Learning, Algorithms, Paris, Global Warming, Renewable Energy
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