Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
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Leveraging AI and ML algorithms for optimizing resource utilization, predicting energy demand, and automating energy management processes can enhance energy efficiency and sustainability in software development.
“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
Sophisticated AI for energy applications demands significant high-performance computing (HPC) resources, leading to high capital/operational costs and substantial energy consumption.
“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]
The significant energy footprint of training and running AI models presents a 'Sustainability Paradox'.
“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]
Successful AI adoption relies on supportive policies and funding, which are often lacking or misaligned.
“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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- 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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