Comparative analysis of ANN and ANFIS models for solar energy prediction: Advancing forecasting accuracy in photovoltaic systems.

In this study, we evaluate the accuracy with which two artificial intelligence models—Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN)—can estimate the output of solar energy in the context of a photovoltaic system. A solar power plant's actual data is employed, and input variables used include various sun radiation and meteorological characteristics. The primary objective is to forecast solar energy output since doing so will help solar energy generation systems be optimised. Performance is assessed using Mean Square Error (MSE) and the Coefficient of Determination (R2). ANFIS's accuracy of 70.42% illustrates how effective it is at locating particular data associations. In terms of prediction accuracy, the ANN model surpasses ANFIS, showing promising accuracy with an R2 of 77.77 and an MSE of 4.8517e+03. The study's results demonstrate that the Artificial Neural Network (ANN) model forecasts solar energy output significantly more precisely than ANFIS, underscoring its potential to improve the dependability and efficiency of solar energy generation systems and deepen our understanding of forecasting renewable energy sources. [ABSTRACT FROM AUTHOR]