Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models

2015 ◽  
Vol 124 (3-4) ◽  
pp. 945-958 ◽  
Author(s):  
Khalil Benmouiza ◽  
Ali Cheknane
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Network Models ◽  
Small Scale ◽  
Neural Network Models ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

scholarly journals Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1691 ◽  
Author(s):  
Fabio Pereira ◽  
Francisco Bezerra ◽  
Shigueru Junior ◽  
Josemir Santos ◽  
Ivan Chabu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Neural Network Models ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

scholarly journals Modeling of Nonlinear Autoregressive Neural Network for Multi-Step Ahead Air Quality Prediction

TEM Journal ◽  
2020 ◽  
pp. 852-861
Author(s):  
Mirza Pasic ◽  
Izet Bijelonja ◽  
Edin Kadric ◽  
Hadis Bajric
Keyword(s):  
Neural Network ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Network Models ◽  
Quality Prediction ◽  
Optimal Model ◽  
Neural Network Models ◽  
Air Quality Prediction ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

In this paper five neural network models were developed using NARX-SP neural network type in order to predict air pollutants concentrations (SO2, PM10, NO2, O3 and CO ) for the 72nd hour ahead for Sarajevo. Hourly values of air pollutants concentrations and meteorological parameters (air temperature, pressure and humidity, wind speed and direction) for Sarajevo were used. Optimal model was selected based on the values of R2, MSE and the complexity of models. Optimal neural network model can predict air pollutants concentrations for the 72nd hour ahead with high accuracy, as well as for all hours up to 72nd hour.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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