Open AccessPrediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks
Publication year - 2018
Publication title -
international journal of thermal and environmental engineering
Language(s) - English
Resource type - Journals
ISSN - 1923-7316
DOI - 10.5383/ijtee.14.02.003
Subject(s) - nonlinear autoregressive exogenous model , artificial neural network , feed forward , autoregressive model , feedforward neural network , matlab , computer science , environmental science , meteorology , artificial intelligence , engineering , mathematics , statistics , control engineering , geography , operating system
In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it.