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

TL;DR

This study compares machine learning models, including a hybrid neuro-fuzzy system and a multilayer perceptron optimized with Grey Wolf Optimizer, for predicting solar diffuse fraction with high accuracy.

Contribution

It introduces a hybrid MLP-GWO model and evaluates its performance against ANFIS and traditional MLP models for solar diffuse fraction prediction.

Findings

MLP-GWO achieved the highest accuracy in predictions.

Hybrid models outperformed traditional single models.

Results demonstrated the effectiveness of GWO optimization in solar irradiance forecasting.

Abstract

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.