Predicting I-V curve for photovoltaic modules using Random Forests Technique

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Alia, Areej Ahmad
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جامعة النجاح الوطنية
The study of the special curves of solar PV modules are of great importance in developing cells and increasing their capacity, hence the idea of this thesis that was intended to predict the current-voltage curve for solar PV module by developing a new developed model that relies on random forest technique in training and testing data using MATLAB program. The random forest technique is a machine learning method, where this technique relies on decision trees (classification trees, regression trees). The regression trees were released in the new proposed model to predict the output variable (PV module output current), depending on a set of inputs represented by five parameters (ambient temperature, solar radiation, PV DC voltage, short circuit current, and open circuit voltage). This data sets were obtained by conducting several experiments on a (STF - 120P6) poly-crystalline PV module 14.0% module efficiency. Seven experiments were done on the (STF - 120P6) PV module in different values of cell temperatures and solar radiations to measure the current and the voltages by using the (I-V CURVE TRACER DEVICE). Through training and testing these data, high accuracy results were obtained for the proposed model, where the metric error values (RMSE, MAPE, and MBE), which are equal (0.04251%, 4.315097%, -0.3959%), respectively. A value of (MAPE) was used to evaluate this model and compare it with previous models that adopted different methods to predict the I-V curve of the solar PV module. These methods can be classified into offline methods and online methods. Online methods depend on real devices to extract the I-V curve as (capacitor, resistor, inductor, and switches), Offline methods represented in Artificial Intelligence methods, Random Forests technique, and the numerical methods where are used to obtain numerical solutions of a mathematical problem such as Levenberg–Marquardt method(LM), Newton–Raphson method (NRM),