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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
ARTICLE IN PRESS (volume, issue and page numbers will be assigned later)
DOI: (registered soon)
Maen Takruri1 , Maissa Farhat1, Sumith Sunil1, Jose Antonio Ramos-Hernanz2, Oscar Barambones3
1 Department of Electrical, Electronics and Communication Engineering, American University of Ras Al Khaimah, UAE
2 Department of Electrical Engineering, University of the Basque Country, Spain
3 Department of Systems and Automatic Engineering, University of the Basque Country, Spain


Photovoltaic panels are promising source for renewable energy.  They serve as a clean source of electricity by converting the radiations 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 accurately capable of driving the Photovoltaic panel to produce optimal output power for a given temperature and irradiation levels.    

Keywords: Support vector regression; machine learning; PV Panel; maximum power point estimation; efficiency; power.

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