Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems

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Hichem Hafdaoui
El Amin Kouadri Boudjelthia
Salim Bouchakour
Nasreddine Belhaouas


The performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an artificial intelligence (AI) method based on machine learning (ML). For more efficient analysis, the Support Vector Machine (SVM) is used to simplify and optimize the processing of these data for the study of the performance of PV systems. More precisely, we group a multi-class data variable according to the needs of the analysis using SVMs. In this article, we present all the stages of data processing based on the application of artificial intelligence (AI). We present as an example the results obtained in the study of the performance of a 150W monocrystalline photovoltaic (PV) module after one year of monitoring.

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How to Cite
H. . Hafdaoui, E. A. . Kouadri Boudjelthia, S. . Bouchakour, and N. . Belhaouas, “Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems”, J. Ren. Energies, vol. 25, no. 2, pp. 199 -, Dec. 2022.


Hafdaoui, Hichem, et al. "Analyzing the performance of photovoltaic systems using support vector machine classifier." Sustainable Energy, Grids and Networks 29 (2022): 100592. doi : 10.1016/j.segan.2021.100592.

YUN, Liu, BOFENG, Yan, DAN, Qian, et al. Research on Fault Diagnosis of Photovoltaic Array Based on Random Forest Algorithm. In: 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). IEEE, 2021. p. 194-198. doi : 10.1109/ICPECA51329.2021.9362559.

HONG, Feng, SONG, Jie, MENG, Hang, et al. A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images. Solar Energy, 2022, vol. 236, p. 406-416. DOI: 10.1016/j.solener.2022.03.018.

LIU, Yongjie, DING, Kun, ZHANG, Jingwei, et al. Intelligent fault diagnosis of photovoltaic array based on variable predictive models and I–V curves. Solar Energy, 2022, vol. 237, p. 340-351. DOI: 10.1016/j.solener.2022.03.062.

BANSAL, Neha, JAISWAL, Shiva Pujan, et SINGH, Gajendra. Long term performance assessment and loss analysis of 9 MW grid tied PV plant in India. Materials Today: Proceedings, 2022. doi : 10.1016/j.matpr.2022.01.263.

DE BENEDETTI, Massimiliano, LEONARDI, Fabio, MESSINA, Fabrizio, et al. Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing, 2018, vol. 310, p. 59-68. doi : 10.1016/j.neucom.2018.05.017.

POLO, Fernando A. Olivencia, BERMEJO, Jesús Ferrero, FERNÁNDEZ, Juan F. Gómez, et al. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy, 2015, vol. 81, p. 227-238. doi : 10.1016/j.renene.2015.03.023.

RUIZ-GONZALEZ, Ruben, GOMEZ-GIL, Jaime, GOMEZ-GIL, Francisco Javier, et al. An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis. Sensors, 2014, vol. 14, no 11, p. 20713-20735. DOI: 10.3390/s141120713.

FARID, Nahla, ELBAGOURY, Bassant, ROUSHDY, MOHAMED, et al. A comparative analysis for support vector machines for stroke patients. Rec Adv Inf Sci, 2013, p. 71-76.

RAY, Sunil, BANSAL, S., GUPTA, A., et al. Understanding Support Vector Machine algorithm from examples (along with code). Analytics Vidhya, 2017, vol. 13, p. 19.

C.J.C. Burges, “A Tutorial On Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998. DOI: 10.1023/A:1009715923555.

B. Scho¨lkopf, I. Guyon, and J. Weston, “Statistical Learning and Kernel Methods in Bioinformatics,” Artificial Intelligence and Heuristic Methods in Bioinformatics 183, P. Frasconi and R. Shamir, eds., pp. 1-21, IOS Press, 2003.

S. Gunn, “Support Vector Machines for Classification and Regression,” ISIS Technical Report MP-TR-98-05, Image Speech and Intelligent Systems Group, Univ. of Southampton, 1998.

K.K. Chin, “Support Vector Machines Applied to Speech Pattern Classification,” master’s thesis, Eng. Dept., Cambridge Univ., 1999.

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge Univ. Press, 1999.

IEC 61853, Photovoltaic (PV) module performance testing and energy rating – Part 1: Irradiance and temperature performance measurements and power rating.