Implementation of Generalized Regression Neural Network (GRNN) for Solar Panel Power Estimation

An efficient algorithm to characterize the current-voltage (IV) curve of photovoltaic (PV) modules under different operating conditions is necessary for solar power estimation and some operational stability. In this paper, the developed algorithm utilizes a generalized regression neural network (GRNN) as a power estimator for solar panels. The proposed model uses the seven input variables namely, the IV characteristic curve, weather condition, and temperature parameters from the testbed solar panel modules of Cheongju University dated August 2019 to July 2020. The dataset is divided into three sections as training, validation, and testing set to 60%, 20%, and 20 %, respectively. The simulation provides comparative results for the actual and predicted power. Moreover, GRNN shows a better predicted power output compared to a feed-forward neural network (FFNN). The regression value results in a much nearer to 1 that fits the dataset. The correlation coefficient is 0.9961 which identifies the provided dataset is on the line of best fit.