Digital Twins for Sustainable Energy Optimization and Predictive Maintenance at Noor Ouarzazate, Morocco
Hicham AGOURAME, Tawfik MASROUR
Abstract. A hybrid digital twin solution was thus created for the Noor Ouarzazate Solar Complex to improve the efficiency and functionality of CSP (Concentrated Solar Power) and PV (Photovoltaic) plants on a larger scale. This project arose from the prolonged shutdown of Noor III because of a molten salt leak. This justifies the need for a better monitoring and maintenance system. It combines simulation models with the help of machine learning and IoT sensors. It thus provides an efficient way for the early detection and anticipation of faults, including molten salt tank failure, in both the thermal and electrical systems. The simulation outcome shows that the digital twin operates with a real-time fault classification performance of 98.5% precision, recall, and F1-score, while reporting key operational indicators including a mean time to repair (MTTR) of 27.3 hours, a mean time between failures (MTBF) of 1 hour, an efficiency of approximately 95%, and a remaining useful life estimate of up to 6 months. In addition, the dashboard provides an indicator of the real-time CO₂ impact of 1929.0 tons within the operating conditions that are being monitored. The proposed model of the digital twin has great potential and can serve as one of the feasible solutions that can improve practices of predictive maintenance in complex systems in the renewable energy industry.
Keywords
Digital Twin, Noor Ouarzazate, Concentrated Solar Power, Predictive Maintenance, Renewable Energy
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Hicham AGOURAME, Tawfik MASROUR, Digital Twins for Sustainable Energy Optimization and Predictive Maintenance at Noor Ouarzazate, Morocco, Materials Research Proceedings, Vol. 64, pp 306-313, 2026
DOI: https://doi.org/10.21741/9781644904091-38
The article was published as article 38 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] OM. S. Abdelkareem, T. A. Abbas, “Concentrated Solar Power Technology: Current Status and Research Trends,” Solar Energy, vol. 216, pp. 264–281, 2021. https://doi.org/10.1016/j.solener.2020.12.065
[2] Solar Energy Resource and Power Generation in Morocco: Current Situation, Potentials and Future Perspective, Resources, vol. 13, no. 10, art. 140, 2024. https://doi.org/10.3390/resources13100140
[3] Mbasso, W. F. (2025). Digital twins in renewable energy systems. ScienceDirect. Retrieved from https://www.sciencedirect.com/science/article/pii/S2211467X25001774
[4] Abdessadak, A. (2025). Digital twin technology and artificial intelligence in energy systems. ScienceDirect. Retrieved from https://www.sciencedirect.com/science/article/pii/S235248472500263X
[5] S. Ma, K. A. Flanigan, M. Bergés, State‑of‑the‑Art Review: The Use of Digital Twins to Support Artificial Intelligence‑Guided Predictive Maintenance, arXiv:2406.13117, 2024.
[6] W. Fendzi Mbasso, A. Harrison, I. Dagal, P. Jangir, M. Khishe, H. Kotb, M. S. Shaikh, A. Smerat, E. F. Donfack, R. Kumar, “Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions,” Energy Strategy Reviews, vol. 61, 101814, 2025. https://doi.org/10.1016/j.esr.2025.101814
[7] A. Elnosh, M. Calais and D. Parlevliet, “A systematic literature review of digital twin research for photovoltaic systems: Trends, challenges, and opportunities,” Renew. Sustain. Energy Rev., vol. 226, p. 116326, Feb. 2026, https://doi.org/10.1016/j.rser.2025.116326
[8] M. Fadel, F. M. Alelaj, “Digital twin-based performance evaluation of a photovoltaic system: A real-time monitoring and optimization framework,” Int. J. Power Electron. Drive Syst., vol. 16, no. 3, pp. 2072-2081, Sep. 2025, doi: 10.11591/ijpeds.v16.i3.pp2072-2081
[9] Y. Zhang, X. Zhao, “Digital twin integration with data fusion for enhanced photovoltaic system management: A systematic literature review,” IEEE Open Journal of Power Electronics, vol. 6, pp. 210–228, 2025.
[10] L. Tran, M. Lee, “AI‑based predictive maintenance of solar photovoltaics systems: A comprehensive review,” Energy Informatics, vol. 8, 128, 2025. https://doi.org/10.1186/s42162-025-00594-6
[11] F. Chen, H. Wu, “A novel digital‑twin approach based on transformer for photovoltaic power prediction,” Scientific Reports, vol. 14, 11234, 2024.
[12] A. Kumar, S. Reddy, “Digital twin integration for photovoltaic–battery energy storage systems: A systematic review,” Future Energy and Environment Letters, vol. 3, no. 2, pp. 45–60, 2025
[13] Z. Chen, J. Li, A. Kumar, “Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions,” Energy Strategy Reviews, vol. 61, 101814, 2025. https://doi.org/10.1016/j.esr.2024.101814
[14] W. F. Fendzi Mbasso, A. Harrison, I. Dagal, P. Jangir, M. Khishe, H. Kotb, M. S. Shaikh, A. Smerat, E. F. Donfack, R. Kumar, “Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions,” Energy Strategy Reviews, vol. 61, 101814, 2025. https://doi.org/10.1016/j.esr.2025.101814
[15] “Digital twin‑driven sustainable energy life cycles: Technical review and guidelines,” Renewable and Sustainable Energy Technology, 2025. https://doi.org/10.53941/rset.2025.100005
[16] W. F. Fendzi Mbasso, A. Harrison, I. Dagal, P. Jangir, M. Khishe, H. Kotb, M. S. Shaikh, A. Smerat, E. F. Donfack, R. Kumar, “Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions,” Energy Strategy Reviews, vol. 61, 101814, 2025. https://doi.org/10.1016/j.esr.2025.101814
[17] M. Kim, F. Ghobadi, A. S. T. Charmchi, M. Lee, J. Lee, “Digital Twins for Clean Energy Systems: A State‑of‑the‑Art Review of Applications, Integrated Technologies, and Key Challenges,” Sustainability, vol. 18, no. 1, 43, 2026. https://doi.org/10.3390/su18010043
[18] “Transient performance modelling of solar tower power plants with molten salt thermal energy storage systems,” Journal of Energy Storage, vol. 97, 112793, 2024. https://doi.org/10.1016/j.est.2024.112793
[19] “Digital twin‑driven sustainable energy life cycles: Technical review and guidelines,” Renewable and Sustainable Energy Technology, 2025. https://doi.org/10.53941/rset.2025.100005
[20] W. F. Fendzi Mbasso, A. Harrison, I. Dagal, P. Jangir, M. Khishe, H. Kotb, M. S. Shaikh, A. Smerat, E. F. Donfack, R. Kumar, “Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions,” Energy Strategy Reviews, vol. 61, 101814, 2025. https://doi.org/10.1016/j.esr.2025.101814
[21] M. Kim, F. Ghobadi, A. S. T. Charmchi, M. Lee, J. Lee, “Digital Twins for Clean Energy Systems: A State‑of‑the‑Art Review of Applications, Integrated Technologies, and Key Challenges,” Sustainability, vol. 18, no. 1, 43, 2026. https://doi.org/10.3390/su18010043
[22] “AI‑based predictive maintenance of solar photovoltaics systems: a comprehensive review,” Energy Informatics, vol. 8, 128, 2025.
[23] “Digital twin‑driven sustainable energy life cycles: Technical review and guidelines,” Renewable and Sustainable Energy Technology, 2025. https://doi.org/10.53941/rset.2025.100005
[24] “Predictive digital twin for wind energy systems: a literature review,” Energy Informatics, 2024.
[25] W. F. Fendzi Mbasso, A. Harrison, I. Dagal, P. Jangir, M. Khishe, H. Kotb, M. S. Shaikh, A. Smerat, E. F. Donfack, R. Kumar, “Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions,” Energy Strategy Reviews, vol. 61, 101814, 2025. https://doi.org/10.1016/j.esr.2025.101814
[26] M. Kim, F. Ghobadi, A. S. T. Charmchi, M. Lee, J. Lee, “Digital Twins for Clean Energy Systems: A State‑of‑the‑Art Review of Applications, Integrated Technologies, and Key Challenges,” Sustainability, vol. 18, no. 1, 43, 2026. https://doi.org/10.3390/su18010043
[27] H. Agourame, “Digital Twin-Enabled Predictive Maintenance,” GitHub repository, 2025. [Online]. Available: https://github.com/hishamag-student/DigitalTwin-Enabled-PredictiveMaintenance
[28] “Digital twin‑driven sustainable energy life cycles: Technical review and guidelines,” Renewable and Sustainable Energy Technology, 2025. https://doi.org/10.53941/rset.2025.100005
[29] “AI‑based predictive maintenance of solar photovoltaics systems: a comprehensive review,” Energy Informatics, vol. 8, 128, 2025.
