AUTHOR=Gupta Ankur Kumar , Singh Rishi Kumar 

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

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1029449

DOI=10.3389/fenrg.2022.1029449

ISSN=2296-598X

ABSTRACT=The work of forecasting solar power is becoming more crucial in directive to regulate the quality of the power and increase the systems reliability as photovoltaic (PV) sites are being integrated into the architecture of power systems at an increasing rate. This paper proposes a metaheuristic model for short-term Photo Voltaic power forecasting that includes Shuffled Frog Leaping Algorithm (SFLA), Principle Component Analysis (PCA) and  Generalised Regression Neural Network (GRNN). In this model, GRNN is implemented to analyse the input parameters after the dimension reduction process and also its parameters get optimised 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 a experimental case of a Bhadla Solar Park installed in Gujarat, India. The accuracy of the proposed model obtained is Nominal Mean Absolute Error (nMAE) 2.3325 and Root Mean square Error (RMSE) is 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 paper.