The DOI matches its Crossref record; 216 of the 218 references it cites were found in a registry or an index.
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Leveraging artificial intelligence (AI) and machine learning (ML) algorithms for optimizing resource utilization, predicting energy demand, and automating energy management processes can enhance energy efficiency and sustainability in software development (Ahmad et al ., 2021).”
From Advancing green computing: Practices, strategies, and impact in modern software development for environmental sustainability (2024) · cites this paper as Ahmad et al ., 2021
In the citing paper
“Sophisticated AI for energy applications demands significant high-performance computing (HPC) resources [3,20], leading to high capital/operational costs and substantial energy consumption [5,18].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [18]
In the citing paper
“The significant energy footprint of training and running AI models presents a 'Sustainability Paradox' [18,42,54,55].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [18]
In the citing paper
“Successful AI adoption relies on supportive policies and funding, which are often lacking or misaligned [18,37].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [18]
Citations
Cites
Searle, J. R. (1980) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00005756 · Cited by 6,954
The role of renewable energy in the global energy transformation
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019) · Energy Strategy Reviews · doi:10.1016/j.esr.2019.01.006 · Cited by 4,973
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads
Pálenský, P., & Dietrich, D. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2158841 · Cited by 2,959
Smart Grid Technologies: Communication Technologies and Standards
Güngör, V. Ç., Sahin, D., Koçak, T., Ergüt, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2166794 · Cited by 2,694
A review of data-driven building energy consumption prediction studies
Amasyali, K., & El-Gohary, N. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.04.095 · Cited by 1,887
Pfenninger, S., & Staffell, I. (2016) · Energy · doi:10.1016/j.energy.2016.08.060 · Cited by 1,691
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203
Artificial intelligence techniques for photovoltaic applications: A review
Mellit, A., & Kalogirou, S. A. (2008) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2008.01.001 · Cited by 1,069
Raza, M. Q., & Khosravi, A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.065 · Cited by 1,061
Research works printed but not linked: 8
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- Hao, 2019, Training a single AI model can emit as much carbon as five cars in their lifetimes - MIT Technology Review, MIT Technol. Rev., 1
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- Jiang, 2020, The role of hydrogen energy in China’s future energy system and preliminary study on the route of re-electrification, Therm. Power Gener., 49, 1
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- Ransbotham, 2017, Is your business ready for artificial intelligence? Jt. BCG-MIT sloan manag, Rev. Surv. impact Artif. Intell. Bus.
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Cited by
Sorted by citations
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711
Self-citationStrategies to save energy in the context of the energy crisis: a review
Farghali, M. R. F., Osman, A. I., Mohamed, I. M. A., Chen, Z., Chen, L., Ihara, I., Yap, P., & Rooney, D. W. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01591-5 · Cited by 760
Javed, A. R., Shahzad, F., Rehman, S. U., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022) · Cities · doi:10.1016/j.cities.2022.103794 · Cited by 645
Optimizing renewable energy systems through artificial intelligence: Review and future prospects
Ukoba, K. O., Olatunji, K. O., Adeoye, E., Jen, T., & Madyira, D. M. (2024) · Energy & Environment · doi:10.1177/0958305x241256293 · Cited by 645
Li, J., Herdem, M. S., Nathwani, J. S., & Wen, J. Z. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100208 · Cited by 413
Metadata
- Authors
- Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H.
- Affiliations
- Jinan University (CN)
- Guangxi University (CN)
- University of Macau (MO)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Ahmad, Tanveer
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 289
- Pages
- 125834
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W3118831975
- Cited by
- 1,258
- References
- 218
- Citations by year
- 2026: 219 · 2025: 373 · 2024: 377 · 2023: 168 · 2022: 80 · 2021: 41
- FWCI
- 48.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 316 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Cleaner Production (acceptedVersion)
- Funders
- Universidade de Macau
- Fundo para o Desenvolvimento das Ciências e da Tecnologia
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Economy, and Technology Trends
- Keywords
- artificial intelligence in energy, sustainable energy, smart grid, solar power generation, hydrogen production, energy efficiency optimization, predictive maintenance, big data, cybersecurity, Internet of Things, energy regulation, machine learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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