Version 1
: Received: 13 September 2020 / Approved: 17 September 2020 / Online: 17 September 2020 (05:46:25 CEST)
How to cite:
Claywell, R.; Laszlo, N.; Imre, F.; Mosavi, A. Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction. Preprints2020, 2020090377. https://doi.org/10.20944/preprints202009.0377.v1
Claywell, R.; Laszlo, N.; Imre, F.; Mosavi, A. Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction. Preprints 2020, 2020090377. https://doi.org/10.20944/preprints202009.0377.v1
Claywell, R.; Laszlo, N.; Imre, F.; Mosavi, A. Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction. Preprints2020, 2020090377. https://doi.org/10.20944/preprints202009.0377.v1
APA Style
Claywell, R., Laszlo, N., Imre, F., & Mosavi, A. (2020). Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction. Preprints. https://doi.org/10.20944/preprints202009.0377.v1
Chicago/Turabian Style
Claywell, R., Felde Imre and Amir Mosavi. 2020 "Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction" Preprints. https://doi.org/10.20944/preprints202009.0377.v1
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.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
