scholarly journals Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

2021 ◽  
Vol 7 (14) ◽  
pp. 2017-2030
Author(s):  
Onur GONCU ◽  
Tahsin KOROGLU ◽  
Naime Filiz OZDIL
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Artificial Neural

An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images

Energy ◽  
2013 ◽  
Vol 61 ◽  
pp. 636-645 ◽  
Author(s):  
Alvaro Linares-Rodriguez ◽  
José Antonio Ruiz-Arias ◽  
David Pozo-Vazquez ◽  
Joaquin Tovar-Pescador
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Satellite Images ◽  
Global Solar Radiation ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

Artificial neural network based prediction model of daily global solar radiation in Morocco

2013 ◽  
Vol 5 (6) ◽  
pp. 063137 ◽  
Author(s):  
R. Mejdoul ◽  
M. Taqi ◽  
N. Belouaggadia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Prediction Model ◽  
Global Solar Radiation ◽  
Artificial Neural

scholarly journals An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

2021 ◽  
Vol 22 (3) ◽  
pp. 1245
Author(s):  
Adi Kurniawan ◽  
Anisa Harumwidiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Percentage Error ◽  
Average Percentage ◽  
Daily Average ◽  
Artificial Neural ◽  
Average Percentage Error ◽  
The City

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

Sign in / Sign up

Export Citation Format

Share Document