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Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
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“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]
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Self-citation
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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