Using Machine Learning for Analysis a Database Outdoor Monitoring of Photovoltaic System

In this paper, a new method for analyzing a databaseofoutdoor monitoring of photovoltaic system using machine learninghas been proposed, a Photovoltaic (PV) module (150 w) located in Algiers has been monitored for 80 days and the obtained results have been analyzed.The researchers face many difficulties one of the most significant ones is in terms of collecting and analyzing the obtained results, especially for long period of time of monitoring. In this paper we proposea new methodto analyze the results by machine learningusing Support Vector Machine (SVM) Classifier. Insuch away, we regroup a data variable to multiclass for according and analysis using SVM. Whichhave presented thoroughly all the classificationsteps. Using method ofartificial intelligence (machine learning), recorded data, and thepower output for a given photovoltaic module (PV) technology, types, and small or large stations under any seasons itmakes analysis and processing easy. Themeasurementsofthisworkswere investigated based on the recorded data by acquisition (Keysight 34972A). The system takes measurements data from sensors in a database ready for analysis. The data was taken from the measurement system from 05h00to 21h00 with irradiation of 50 W/m2, which is a starting point, however in 0 to 50 W/m2the system cannot detect any photovoltaic effect. Results predict that the performance ratio (PR) from a Poly-crystallinePV module was around 85.28 % for a different season’s exposure and 727 point analyses at irradiation of 850-950 W/m2at the same time 14h00-15h00. The temperature of a photovoltaic PV module isas wellcalculated and compared with different irradiation and time.