@proceedings {7242,
	title = {Machine Learning for Photovoltaic Systems Condition Monitoring: A Review},
	journal = {47th Annual Conference of the IEEE Industrial Electronics Society, IECON},
	year = {2021},
	address = {Toronto, Canada},
	abstract = {Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.},
	url = {https://ieeexplore.ieee.org/document/9589423},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Mouss, Leila-Hayet}
}
