  The accurate prediction of the solar Diffuse Fraction (DF), sometimes called
the Diffuse Ratio, is an important topic for solar energy research. In the
present study, the current state of Diffuse Irradiance research is discussed
and then three robust, Machine Learning (ML) models, are examined using a large
dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models
used herein, are a hybrid Adaptive Network-based Fuzzy Inference System
(ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer
Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their
predictive precision, using various Solar and Diffuse Fraction (DF) irradiance
data, from Spain. The results were then evaluated using two frequently used
evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square
Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS
model, provided a higher performance, in both the training and the testing
procedures.
