The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.
Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives
Open accessAbstract
The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bio-inspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.
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“Concurrently, as AI applications mature within specific domains, the challenge of coordinating across multiple energy vectors (e.g., electricity, gas, heat) has emerged as a key frontier [36,38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
In the citing paper
“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 [38]
In the citing paper
“AI offers potential for optimizing increasingly integrated multi-energy systems (electricity, heat, gas, hydrogen) [37,38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
In the citing paper
“AI in critical energy decisions raises profound ethical concerns regarding algorithmic bias, data privacy, and accountability [38,62].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
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Cites
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
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Kebede, A. A., Kalogiannis, T., Mierlo, J. V., & Berecibar, M. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112213 · Cited by 1,249
Artificial neural networks in renewable energy systems applications: a review
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Eltawil, M., & Zhao, Z.-M. (2010) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2009.07.015 · Cited by 1,081
A review of wind speed and wind power forecasting with deep neural networks
Wang, Y., Zou, R., Liu, F., Zhang, L., & Liu, Q. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117766 · Cited by 1,019
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Bourdeau, M., Zhai, X. Q., Nefzaoui, E., Guo, X., & Chatellier, P. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101533 · Cited by 860
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Research works printed but not linked: 3
- Zhou, 2022, Chapter 8 - advanced renewable dispatch with machine learning-based hybrid demand-side controller: the state of the art and a novel approach, 237
- Zhou, 2022, Chapter 9 - a machine learning-based design approach on PCMs-PV systems with multilevel scenario uncertainty, 257
- Casini, 2022, Chapter 12 - smart buildings and smart cities, 607
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Synergizing Human Intelligence and Artificial Intelligence for Zero-Carbon Industrialization
Shaw, R., Singh, R., Bathini, H. B., & Kumar, V. (2026) · Disaster risk reduction · doi:10.1007/978-981-95-8153-5_13
Xuan, V. N., Nguyen, T. L. A., Luong, H. G., Hoa, P. X., & Nguyen, T. T. T. (2026) · Environmental and Sustainability Indicators · doi:10.1016/j.indic.2026.101580
AI Analytics for Assessing the Performance of Green Equity Market
Gupta, S. S., Soni, B., & Arockiam, D. (2026) · Wiley eBooks · doi:10.1002/9781394271887.ch5
A systematic review of artificial intelligence driven energy management in smart grids
Kumar, S., Pandey, A. K., Savitha, P. B., Nandini, K. K., Agajo, J., & Giri, N. C. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02607-z
Zhou, Y. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34029-1.00004-9
Self-citation
Metadata
- Authors
- Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G.
- Affiliations
- Hunan University (CN)
- Concordia University (CA)
- Hong Kong Polytechnic University (HK)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Liu, Zhengxuan
- Sun, Ying
- Zhou, Yuekuan
- Venue
- Energy and AI
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 10
- Pages
- 100195
- ISSN
- 2666-5468 (ISSN-L)
- OpenAlex
- W4289861147
- Cited by
- 288
- References
- 174
- Citations by year
- 2026: 60 · 2025: 99 · 2024: 91 · 2023: 34 · 2022: 4
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energy and AI (publishedVersion, cc-by)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
- Physical Sciences › Energy › Energy Engineering and Power Technology › Hybrid Renewable Energy Systems
- Keywords
- artificial intelligence, large-scale renewable energy integration, integrated energy systems, carbon neutrality, bio-inspired optimization, operational control, decarbonization, renewable energy forecasting, energy management
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