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The accurate prediction of the solar Diffuse Fraction (DF), sometimes calledthe Diffuse Ratio, is an important topic for solar energy research. In thepresent study, the current state of Diffuse Irradiance research is discussedand then three robust, Machine Learning (ML) models, are examined using a largedataset (almost 8 years) of hourly readings from Almeria, Spain. The ML modelsused herein, are a hybrid Adaptive Network-based Fuzzy Inference System(ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-LayerPerceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for theirpredictive precision, using various Solar and Diffuse Fraction (DF) irradiancedata, from Spain. The results were then evaluated using two frequently usedevaluation criteria, the Mean Absolute Error (MAE) and the Root Mean SquareError (RMSE). The results showed that the MLP-GWO model, followed by the ANFISmodel, provided a higher performance, in both the training and the testingprocedures.

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction