Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

2013 ◽  
Vol 75 ◽  
pp. 561-569 ◽  
Author(s):  
Khalil Benmouiza ◽  
Ali Cheknane
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Network Models ◽  
Global Solar Radiation ◽  
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

Application of nonlinear autoregressive neural network to estimation of global solar radiation over Nigeria

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Olusola Samuel Ojo ◽  
Babatunde Adeyemi
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Surface Data ◽  
Narx Models ◽  
Sahel Region ◽  
Nonlinear Autoregressive ◽  
Narx Model ◽  
Mlr Model ◽  
Autoregressive Neural Network

In this paper, surface data meteorological were used as input variables to create, train and validate the network in which global solar radiation serves as a target. These surface data were obtained from the archives of the European centre for Medium-Range weather forecast for a span of 36 years (1980-2015) over Nigeria. The research aims to evaluate the predictive ability of the nonlinear autoregressive neural network with exogenous input (NARX) model compared with the multivariate linear regression (MLR) model using the statistical metrics. Model selection analysis using the index of agreement (dr) metric showed that the MLR and NARX models have values of 0.710 and 0.853 in the Sahel, 0.748 and 0.849 in the Guinea Savannah, 0.664 and 0.791 in the Derived Savannah, 0.634 and 0.824 in the Coastal regions, and 0.771 and 0.806 in entire Nigeria respectively. Meanwhile, error analyses of the models using root mean square errors (RMSE) showed the values of 1.720 W/m2 and 1.417 in the Sahel region, 2.329 W/m2 and 1.985 W/m2 in the Guinea Savannah region, 2.459 W/m2 and 2.272 W/m2 in the Derived Savannah region, 2.397 W/m2 and 2.261 W/m2 in the Coastal region and 1.691 W/m2 and 1.600 W/m2 in entire Nigeria for MLR and NARX models respectively. These showed that the NARX model has higher dr values and lower RMSE values over all the climatic regions and entire Nigeria than the MLR model. Finally, it can be inferred from these metrics that the NARX model gives a better prediction of global solar radiation than the traditional common MLR models in all the zones in Nigeria.

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.

Sign in / Sign up

Export Citation Format

Share Document