Short-term day-ahead photovoltaic output forecasting using PCA-SFLA-GRNN algorithm

Abstract

The work of forecasting solar power is becoming more crucial with directives to regulate the quality of the power and increase the system’s reliability as photovoltaic (PV) sites are being integrated into the architecture of power systems at an increasing rate. This study proposes a metaheuristic model for short-term photovoltaic power forecasting that includes shuffled frog leaping algorithm (SFLA), principal component analysis (PCA), and generalized regression neural network (GRNN). In this model, GRNN is implemented to analyze the input parameters after the dimension reduction process, and its parameters get optimized with the help of the SFLA, which has the advantage of fast convergence speed as well as searching ability, whereas PCA techniques are implemented to diminish the dimension of meteorological conditions. This hybrid model achieves day-ahead short-term forecasting, as shown in an experimental case of a Bhadla Solar Park installed in Gujarat, India. The accuracy of the proposed model obtained a mean absolute error (nMAE) of 2.3325, and a root mean square error (RMSE) of 129.425. Similarly, the error in forecasting obtained by the proposed method results in nMAE = 2.977 and RMSE = 160.92. The output results obtained surpassed all other hybrid models used for comparison in this study.