Using Orthogonal Grey Wolf Optimizer with Mutation for Training Multi-Layer Perceptron Neural Network

2016 ◽  
Vol 13 (7) ◽  
pp. 4544-4556 ◽  
Sen Zhang ◽  
Yongquan Zhou ◽  
Junmin Song ◽  
Chengyan Zhao
Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1192
Randall Claywell ◽  
Laszlo Nadai ◽  
Imre Felde ◽  
Sina Ardabili ◽  
Amirhosein Mosavi

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.

2021 ◽  
Vol 220 ◽  
pp. 106639
Amirhosein Mosavi ◽  
Saeed Samadianfard ◽  
Sabereh Darbandi ◽  
Narjes Nabipour ◽  
Sultan Noman Qasem ◽  

Sign in / Sign up

Export Citation Format

Share Document