Recurrent Neural Networks (LSTM and GRU) in the Prediction of Current–Voltage Characteristics Curves of Polycrystalline Solar Cells

The current–voltage (I-V) characteristic provides essential performance parameters of a solar cell, influenced by temperature and solar radiation. The efficiency of a solar cell is sensitive to variations in these conditions. This study electrically characterized a polycrystalline silicon solar cell in a solar simulator chamber at temperatures of 25–55 °C and irradiance levels of 600–1000 W/m2. The acquired data were used to train and evaluate neural network models to predict the I-V characteristics of a polycrystalline silicon solar cell. Two recurrent neural network architectures were tested: LSTM and the GRU model. The performance of the model was assessed using MAE, RMSE, and R2. The GRU model achieved the results, with MAE = 2.813×10−3, RMSE = 5.790×10−3, and R2 = 0.9844, similar to LSTM (MAE = 2.6613×10−3, RMSE = 5.858×10−3, R2 = 0.9840). These findings highlight the GRU network as the most efficient approach for modeling solar cell behavior under varying environmental conditions.