The DOI matches its Crossref record; 115 of the 117 references it cites were found in a registry or an index.
Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions
Open accessAbstract
Abstract Currently, a huge number of photovoltaic plants have been installed worldwide and these plants should be carefully protected and supervised continually in order to be safe and reliable during their working lifetime. Photovoltaic plants are subject to different types of faults and failures, while available fault detection equipment are mainly used to protect and isolate the photovoltaic plants from some faults (such as arc fault, line-to-line, line-to-ground and ground faults). Although a good number of international standards (IEC, NEC, and UL) exists, undetectable faults continue to create serious problems in photovoltaic plants. Thus, designing smart equipment, including artificial intelligence and internet of things for remote sensing and fault detection and diagnosis of photovoltaic plants, will considerably solve the shortcomings of existing methods and commercialized equipment. This paper presents an overview of artificial intelligence and internet of things applications in photovoltaic plants. This research presents also the most advanced algorithms such as machine and deep learning, in terms of cost implementation, complexity, accuracy, software suitability, and feasibility of real-time applications. The embedding of artificial intelligence and internet of things techniques for fault detection and diagnosis into simple hardware, such as low-cost chips, may be economical and technically feasible for photovoltaic plants located in remote areas, with costly and challenging accessibility for maintenance. Challenging issues, recommendations, and trends of these techniques will also be presented in this paper.
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“an energy sector that is inherently conservative due to the high stakes associated with system reliability and stringent regulatory compliance [10]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [10]
In the citing paper
“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 [10]
In the citing paper
“Standard AI struggles with novel or rare events (e.g., extreme weather, cyber-physical attacks as in Section 3.1.3),necessitating advanced FDD and adaptive learning, though ensuring the safety of such adaptive systems is a research and operational challenge (Section 3.3; [10,49]).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [10]
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“Data privacy with smart meter/IoT data is crucial [10,34], as breaches are linked to cybersecurity risks (Section 3.1.3).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [10]
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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
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Research works printed but not linked: 2
- Hamied, 2019, IoT-based smart photovoltaic arrays for remote sensing and fault identification. International conference in artificial intelligence in renewable energetic systems, 478
- Dupont, 2019, 200, 112078
Cited by
Failures of Photovoltaic modules and their Detection: A Review
Akram, M. W., Li, G., Jin, Y., & Chen, X. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118822 · Cited by 212
Photovoltaic system fault detection techniques: a review
El‐Banby, G. M., Moawad, N. M., Abouzalm, B. A., Abouzaid, W. F., & Ramadan, E. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09041-7 · Cited by 218
Yap, K. Y., Chin, H. H., & Klemeš, J. J. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112862 · Cited by 179
Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Overview of Demand-Response Services: A Review
Stanelytė, D., Radziukynienė, N., & Radziukynas, V. (2022) · Energies · doi:10.3390/en15051659 · Cited by 169
Metadata
- Authors
- Mellit, A., & Kalogirou, S.
- Affiliations
- University of Jijel (DZ)
- Cyprus University of Technology (CY)
- Corresponding author
- Mellit, Adel
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 143
- Pages
- 110889
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3134391797
- Cited by
- 280
- References
- 117
- Citations by year
- 2026: 45 · 2025: 69 · 2024: 61 · 2023: 49 · 2022: 38 · 2021: 17
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- green
- ktisis.cut.ac.cy
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- photovoltaic systems, fault detection and diagnosis, Internet of Things, artificial intelligence, remote sensing, machine learning, deep learning, low-cost hardware, remote areas
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- CoChat. (2026). Citation check: Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. https://app.cochat.ai/papers/10.1016/j.rser.2021.110889
- The paper
- Mellit, A., & Kalogirou, S. (2021). Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2021.110889
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
