Employing machine learning by classification for analysis of a monitoring database from a photovoltaic module

The use of artificial intelligence methods in data analysis facilitates and shortens the time for making decisions, especially when faults or malfunctions occur at the photovoltaic station level. The research-ers face many difficulties. One of the most significant ones is in terms of collecting and analyzing the obtained results, especially for a long period of monitoring. This paper proposes a new method to analyze the results by classification using a support vector machine (SVM) classifier. In such a way, a data variable is regrouped into a multiclass for analysis using SVM. Based on the applica-tion of artificial intelligence (classification), recorded data, the power output for a given photovol-taic module (PV) technology, types, and small or large stations under any season can be analyzed and processed easily. In this paper, classification was employed to analyze the monitoring database of a photovoltaic (PV) module (260 W) over 5 months.