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]
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Cites
Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
A review of renewable energy sources, sustainability issues and climate change mitigation
Owusu, P. A., & Sarkodie, S. A. (2016) · Cogent Engineering · doi:10.1080/23311916.2016.1167990 · Cited by 3,032
Big Data and Machine Learning in Health Care
Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506
Machine learning methods for solar radiation forecasting: A review
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836
Optimization methods applied to renewable and sustainable energy: A review
Baños, R., Manzano-Agugliaro, F. R., Montoya, F. G., Gil, C., Alcayde, A., & Gómez, J. C. L. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2010.12.008 · Cited by 1,649
The National Solar Radiation Data Base (NSRDB)
Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G. J., & Shelby, J. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.003 · Cited by 1,466
Qazi, A., Fayaz, H., Rahim, N. A., Hardaker, G., Alghazzawi, D., Shaban, K. B., & Haruna, K. (2019) · IEEE Access · doi:10.1109/access.2019.2906402 · Cited by 1,179
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Gu, T., Dolan-Gavitt, B., & Garg, S. (2017) · arXiv · doi:10.48550/arxiv.1708.06733 · Cited by 1,061
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
The security of machine learning
Barreno, M., Nelson, B. A., Joseph, A. D., & Tygar, J. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5188-5 · Cited by 839
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2 of the 174 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Kumar, 2021, Application of machine learning for hydropower plant silt data analysis, Mater. Today: Proc., 46, 5575
- Namiot, 2021, Military applications of machine learning, Int. J. Open Inform. Technol., 10, 69
- Ozbas, 2019, Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms, Int. J. Hydrogen Energy, 44, 17260, 10.1016/j.ijhydene.2019.02.108 · DOI
- Samadianfard, 2018, Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation, J. Energy Syst., 2, 180, 10.30521/jes.458328 · DOI
- Sharifzadeh, 2019, Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian process regression, Renew. Sustain. Energy Rev., 108, 513, 10.1016/j.rser.2019.03.040 · DOI
- Voyant, 2017, Machine learning methods for solar radiation forecasting: A review, Renew. Energy, 105, 569, 10.1016/j.renene.2016.12.095 · DOI · Paper page
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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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