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Support Vector Machine for Photovoltaic System Efficiency Improvement

Original scientific paper

Journal of Sustainable Development of Energy, Water and Environment Systems
Volume 8, Issue 3, September 2020, pp 441-451
DOI: https://doi.org/10.13044/j.sdewes.d7.0275
Maen Takruri1 , Maissa Farhat1, Sumith Sunil1, Jose A. Ramos-Hernanz2, Oscar Barambones3
1 Department of Electrical, Electronics and Communication Engineering, American University of Ras Al Khaimah, American University of Ras Al Khaimah Road, Ras al Khaimah, United Arab Emirates
2 Department of Electrical Engineering, University of the Basque Country, Barrio Sarriena, s/n, 48940 Lejona, Vizcaya, Spain
3 Department of Systems and Automatic Engineering, University of the Basque Country, Barrio Sarriena, s/n, 48940 Lejona, Vizcaya, Spain


Photovoltaic panels are promising source for renewable energy. They serve as a clean source of electricity by converting the radiation coming from the sun to electric energy.  However, the amount of energy produced by the photovoltaic panels is dependent on many variables including the irradiation and the ambient temperature, leading to nonlinear characteristics. Finding the optimal operating point in the photovoltaic characteristic curve and operating the photovoltaic panels at that point ensures improved system efficiency. This paper introduces a unique method to improve the efficiency of the photovoltaic panel using Support Vector Machines. The dataset, which is obtained from a real photovoltaic setup in Spain, include temperature, radiation, output current, voltage and power for a period of one year. The results obtained show that the system is capable of accurately driving the photovoltaic panel to produce optimal output power for a given temperature and irradiation levels.

Keywords: Photovoltaic panel, Maximum power point estimation, Efficiency, Support vector regression, Machine learning.

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