Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions
Open accessAbstract
Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end-users is strongly dependent on an effective and fault-resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data-related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures.
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Poor data quality causes economic losses from suboptimal decisions.
“causes economic losses from suboptimal decisions (Section 3.2; [11])”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
AI adoption challenges include technical barriers such as data integrity and cybersecurity.
“technical barriers such as data integrity and cybersecurity [11,12]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
Poor data quality (sensor noise, incomplete/heterogeneous datasets) is a primary bottleneck, leading to skewed AI predictions and hampering applications like renewable forecasting.
“Poor data quality (sensor noise, incomplete/heterogeneous datasets) is a primary bottleneck, leading to skewed AI predictions [5,11,19] and hampering applications like renewable forecasting [4,10].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
Foundational challenges such as data quality, cybersecurity, and high costs have persisted as core concerns throughout the 2018-2025 period.
“Foundational challenges such as data quality, cybersecurity, and high costs have persisted as core concerns throughout the 2018-2025 period (e.g., [11,24,35]).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
Uncertain returns can lead to risk aversion and delayed adoption.
“can lead to risk aversion and delayed adoption given uncertain returns [11,13]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
Energy systems' dynamism necessitates AI models capable of sophisticated reasoning and robust fault handling, but model complexity and generalizability are significant challenges.
“Energy systems' dynamism necessitates AI models capable of sophisticated reasoning and robust fault handling [11], but model complexity and generalizability are significant challenges [48].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
Without a solid data infrastructure and clear governance policies, even the most advanced models are likely to fail or produce biased outcomes.
“Without a solid data infrastructure and clear governance policies, even the most advanced models are likely to fail or produce biased outcomes, a challenge consistently highlighted in the energy context [5,11].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [11]
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Cited by
Sorted by citations
Alam, M. M., Hossain, M. J., Habib, M. A., Arafat, M. Y., & Hannan, M. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115251 · Cited by 76 · see citing works
- Authors
- Afridi, Y. S., Ahmad, K., & Hassan, L.
- Affiliations
- University of Engineering and Technology Peshawar (PK)
- Hamad bin Khalifa University (QA)
- Corresponding author
- Afridi, Yasir Saleem
- Venue
- International Journal of Energy Research
- Publisher
- Wiley
- Published
- 2021-07-31
- Volume
- 46
- Issue
- 15
- Pages
- 21619-21642
- ISSN
- 0363-907X (ISSN-L)
- 1099-114X
- OpenAlex
- W3157656588
- Cited by
- 140
- References
- 133
- Citations by year
- 2026: 43 · 2025: 54 · 2024: 24 · 2023: 13 · 2022: 3 · 2021: 3
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Journal of Energy Research (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Power System Reliability and Maintenance
- Keywords
- predictive maintenance, renewable energy systems, artificial intelligence, machine learning, fault detection, operation and maintenance, hybrid generation system, data quality, data auditing, feature engineering, interpretability, data security
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