Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction
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...
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MDPI AG
2020-10-01
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| Online Access: | https://www.mdpi.com/1099-4300/22/11/1192 |
| _version_ | 1797550199788273664 |
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| author | Randall Claywell Laszlo Nadai Imre Felde Sina Ardabili Amirhosein Mosavi |
| author_facet | Randall Claywell Laszlo Nadai Imre Felde Sina Ardabili Amirhosein Mosavi |
| author_sort | Randall Claywell |
| collection | DOAJ |
| description | 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 eight 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 DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) 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. |
| first_indexed | 2024-03-10T15:26:08Z |
| format | Article |
| id | doaj.art-f5b1f76d98224b5d9bcb3023fd47a501 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T15:26:08Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f5b1f76d98224b5d9bcb3023fd47a5012023-11-20T18:05:40ZengMDPI AGEntropy1099-43002020-10-012211119210.3390/e22111192Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse FractionRandall Claywell0Laszlo Nadai1Imre Felde2Sina Ardabili3Amirhosein Mosavi4Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, HungaryKando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, HungaryJohn von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, HungaryKando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, HungaryEnvironmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, VietnamThe 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 eight 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 DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) 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.https://www.mdpi.com/1099-4300/22/11/1192machine learningpredictionadaptive neuro-fuzzy inference systemadaptive network-based fuzzy inference systemdiffuse fractionmultilayer perceptron (MLP) |
| spellingShingle | Randall Claywell Laszlo Nadai Imre Felde Sina Ardabili Amirhosein Mosavi Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction Entropy machine learning prediction adaptive neuro-fuzzy inference system adaptive network-based fuzzy inference system diffuse fraction multilayer perceptron (MLP) |
| title | Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction |
| title_full | Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction |
| title_fullStr | Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction |
| title_full_unstemmed | Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction |
| title_short | Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction |
| title_sort | adaptive neuro fuzzy inference system and a multilayer perceptron model trained with grey wolf optimizer for predicting solar diffuse fraction |
| topic | machine learning prediction adaptive neuro-fuzzy inference system adaptive network-based fuzzy inference system diffuse fraction multilayer perceptron (MLP) |
| url | https://www.mdpi.com/1099-4300/22/11/1192 |
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