Method of monitoring and detection of failures in PV system based on machine learning




artificial intelligence, renewable energy sources, monitoring


Machine learning methods have been used to solve complicated practical problems in different areas and are becoming increasingly popular today. The purpose of this article is to evaluate the prediction of the energy production of three different photovoltaic systems and the supervision of measurement sensors, through Machine learning and data mining in response to the behavior of the climatic variables of the place under study. On the other hand, it also includes the implementation of the resulting models in the SCADA system through indicators, which will allow the operator to actively manage the electricity grid. It also offers a strategy in simulation and prediction in real-time of photovoltaic systems and measurement sensors in the concept of smart grids.

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Author Biographies

Darío Javier Benavides, University of Málaga

Msc, University of Málaga.

Paul Arévalo-Cordero, University of Cuenca

MsC. University of Jaén, Linares Scientific-Technological Campus and University of Cuenca, Balzay Technological Campus.

Luis G. González, University of Cuenca

PhD. University of Cuenca, Balzay Technological Campus.

Luis Hernández-Callejo, University of Valladolid

PhD., Professor, Campus de la Universidad de Soria, University of Valladolid.

Francisco Jurado, University of Jaén

PhD. University of Jaén. Linares Scientific-Technological Campus.

José A. Aguado, University of Málaga

PhD. University of Málaga.


C. Voyant and et al, “Machine learning methods for solar radiation forecasting: A review,” Renewable Energy, vol. 105, May 2017. [Online]. Available:

S. Theocharides, G. Makrides, G. E. Georghiou, and A. Kyprianou, “Machine learning algorithms for photovoltaic system power output prediction,” in 2018 IEEE International Energy Conference (ENERGYCON), Limassol, Cyprus, 2018.

C. Kurien and A. K. Srivastava, “Scope of artificial intelligence techniques for exhaust emission prediction of CI engines and renewable energy applications„” International Journal of Engineering Research in Computer Science and Engineering, vol. 5, no. 2, pp. 456–461, Feb 2018.

S. Preda, S. Vasilica, A. Bâra, and A. Belciu, “PV forecasting using support vector machine learning in a big data analytics context,”Symmetry, vol. 10, no. 12, December 2018. [Online]. Available:

T. T. Teo, T. Logenthiran, W. L. Woo, and K. Abidi, “Forecasting of photovoltaic power using regularized ensemble extreme learning machine,” in 2016 IEEE Region 10 Conference (TENCON), Singapore, Singapore, 2017, pp. 455–458.

T. Huuhtanen and A. Jung, “Predictive maintenance of photovoltaic panels via deep learning,” in 2018 IEEE Data Science Workshop (DSW), Lausanne, Switzerland, 2018.

H. T. Pedro, C. F. Coimbra, M. David, and P. Lauret, “Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts,” Symmetry, vol. 123, August 2018. [Online]. Available:

M. N. Akhter, S. Mekhilef, H. Mokhlis, and N. M. Shah, “Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques,” IET Renewable Power Generation, vol. 13, no. 7, May 9 2019. [Online]. Available:

C. Tu, X. He, Z. Shuai, and F. Jiang, “Big data issues in smart grid – A review,” Renewable and Sustainable Energy Reviews, vol. 79, November 2017. [Online]. Available:

S. Siniscalchi and F. D. Bianchi and M. De Prada and C. Ocampo, “A wind farm control strategy for power reserve maximization,”Renew. Energy, vol. 131, February 2019. [Online]. Available:

F. Han and et al, “An intelligent fault diagnosis method for pv arrays based on an improved rotation forest algorithm,”Energy Procedia, vol. 158, February 2019. [Online]. Available:

E. Garoudja, A. Chouder, K. Kara, and S. Silvestre, “An enhanced machine learning based approach for failures detection and diagnosis of PVsystems,” Energy Procedia, vol. 158, February 2019. [Online]. Available:

D. Benavides, P. Arévalo, L. G. Gonzalez, L. Hernández, and F. Jurado, “Machine learning data applied to monitoring PV systems: A case study*,” in Ibero-American Congress of Smart Cities (ICSC-CITIES 2019), Soria, Spain, 2019, pp. 456–470.

S. K. Jha, J. Bilalovic, A. Jha, N. Patel, and H. Zhang, “Renewable energy: Present research and future scope of Artificial Intelligence,”Renewable and Sustainable Energy Reviews, vol. 77, September 2017. [Online]. Available:

J. Li, J. K. Ward, J. Tong, L. Collins, and G. Platt, “Machine learning for solar irradiance forecasting of photovoltaic system,”Renew. Energy, vol. 90, May 2016. [Online]. Available:

M. K. Behera, I. Majumder, and N. Nayak, “Solar photovoltaic power forecasting using optimized modified extreme learning machine technique,” Eng. Sci. Technol. an Int. J., vol. 21, no. 3, June 2018. [Online]. Available:

J. L. Espinoza, L. G. González, and R. Sempértegui, “Micro grid laboratory as a tool for research on non-conventional energy sources in Ecuador,” in 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico, 2018.

D. J. Benavides, F. Jurado, and L. G. González, “Data analysis and tools applied to modeling and simulation of a PV system in Ecuador,” Enfoque UTE, vol. 9, no. 4, 2018. [Online]. Available:

MathWorks. Machine Learning with MATLAB. The MathWorks, Inc. Accessed Nov. 6, 2019. [Online]. Available:




How to Cite

Benavides, D. J., Arévalo-Cordero, P., González, L. G., Hernández-Callejo, L., Jurado, F., & Aguado, J. A. (2021). Method of monitoring and detection of failures in PV system based on machine learning. Revista Facultad De Ingeniería Universidad De Antioquia, (102), 26–43.

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