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Engineering Proceedings
Volume 144
Issue 1
10.3390/engproc2026144006
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Proceeding Paper

Field-Validated Predictive Maintenance Framework for Desert PV Systems Using Machine Learning and Reinforcement Learning †

by
Amjad Ech-Charqaouy
*,
Sidi Salah Ech-Charqaouy
,
Abdelkader Boulezhar
,
Nizar Ech-Charqaouy
and
Redouane Mihramane
Department of Physical Sciences, Faculty of Sciences Ain Chock (FSAC), Hassan II University of Casablanca, Casablanca 20100, Morocco
*
Author to whom correspondence should be addressed.
†
Presented at the 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment, Al Hoceima, Morocco, 28–30 April 2026.
Eng. Proc. 2026, 144(1), 6; https://doi.org/10.3390/engproc2026144006
Published: 25 June 2026
(This article belongs to the Proceedings of 2nd International Conference on Sciences and Techniques for Renewable Energy and the Environment)
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Abstract

Photovoltaic systems in desert environments face severe degradation due to heat and dust, making predictive maintenance essential. Unlike existing approaches limited to fault diagnosis, this work proposes a fully integrated closed-loop framework combining IoT monitoring, machine learning diagnostics, and reinforcement learning decision-making. The approach is validated using six months of real data from a 20 MW PV plant in Boujdour, Morocco (500,000 records). Results show 96.3% diagnostic accuracy, 22% downtime reduction, 18% fewer unnecessary interventions, and a 12% performance ratio improvement, demonstrating enhanced reliability and economic efficiency.
Keywords: photovoltaic systems; predictive maintenance; reinforcement learning; machine learning; IoT monitoring photovoltaic systems; predictive maintenance; reinforcement learning; machine learning; IoT monitoring

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MDPI and ACS Style

Ech-Charqaouy, A.; Ech-Charqaouy, S.S.; Boulezhar, A.; Ech-Charqaouy, N.; Mihramane, R. Field-Validated Predictive Maintenance Framework for Desert PV Systems Using Machine Learning and Reinforcement Learning. Eng. Proc. 2026, 144, 6. https://doi.org/10.3390/engproc2026144006

AMA Style

Ech-Charqaouy A, Ech-Charqaouy SS, Boulezhar A, Ech-Charqaouy N, Mihramane R. Field-Validated Predictive Maintenance Framework for Desert PV Systems Using Machine Learning and Reinforcement Learning. Engineering Proceedings. 2026; 144(1):6. https://doi.org/10.3390/engproc2026144006

Chicago/Turabian Style

Ech-Charqaouy, Amjad, Sidi Salah Ech-Charqaouy, Abdelkader Boulezhar, Nizar Ech-Charqaouy, and Redouane Mihramane. 2026. "Field-Validated Predictive Maintenance Framework for Desert PV Systems Using Machine Learning and Reinforcement Learning" Engineering Proceedings 144, no. 1: 6. https://doi.org/10.3390/engproc2026144006

APA Style

Ech-Charqaouy, A., Ech-Charqaouy, S. S., Boulezhar, A., Ech-Charqaouy, N., & Mihramane, R. (2026). Field-Validated Predictive Maintenance Framework for Desert PV Systems Using Machine Learning and Reinforcement Learning. Engineering Proceedings, 144(1), 6. https://doi.org/10.3390/engproc2026144006

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