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The 9th International Energy Conference
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:: Volume 19, Issue 1 (4-2016) ::
IJE 2016, 19(1): 0-0 Back to browse issues page
Estimation of global solar radiation using artificial neural network in Kermanshah province
Yasser Vasseghian *
Razi University , y_vasseghian@yahoo.com
Abstract:   (4441 Views)

The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Kermanshah province. For this purpose, the meteorological data of 23 stations spread in Kermanshah province along the years 2008–2013 were used as training (17 stations) and testing (6 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 3.98% and correlation coefficient (R) value was obtained to be about 0.9961 for the testing data set.

Keywords: Artificial Neural Network, Multi-Nonlinear Regression, Global Solar Radiation, Kermanshah Province.
Full-Text [PDF 981 kb]   (1815 Downloads)    
Type of Study: Research | Subject: Renewable Energy Technologies
Received: 2015/02/25 | Accepted: 2016/03/14 | Published: 2016/07/20
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Vasseghian Y. Estimation of global solar radiation using artificial neural network in Kermanshah province. IJE 2016; 19 (1)
URL: http://necjournals.ir/article-1-828-en.html


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Volume 19, Issue 1 (4-2016) Back to browse issues page
نشریه انرژی ایران Iranian Journal of Energy
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