Artificial Neural Networks based Prediction of Insolation on Horizontal Surfaces for Bangladesh

Abstract In this work, Artificial Neural Network (ANN) based model for predicting the solar radiation in Bangladesh has been developed. Standard multilayer, feed-forward, back-propagation neural networks with different architecture have been designed using MATLAB's Neural Network tool. The training and testing data of 64 different locations spread all over Bangladesh were obtained from the NASA surface meteorology and solar energy database. The input parameters for the network are: latitude, longitude, elevation, month, average daylight hours, mean earth temperature and relative humidity while the solar insolation on horizontal surfaces are the target parameters. The overall Mean Square Errors (MSE) during training 0.0029, regression value of 0.99707, small percentage of error (0.16% to 1.71%) in response to unknown input vectors indicate that the developed model can be used reliably for predicting insolation of locations where there is no direct irradiance measuring instruments.

[1]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[2]  O. Şenkal,et al.  Estimation of solar radiation over Turkey using artificial neural network and satellite data , 2009 .

[3]  S. Rehman,et al.  Artificial neural network estimation of global solar radiation using air temperature and relative humidity , 2008 .

[4]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[5]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[6]  S. Reddy,et al.  An empirical method for the estimation of net radiation intensity , 1971 .

[7]  J. Glover,et al.  The empirical relation between solar radiation and hours of bright sunshine in the high‐altitude tropics , 1958 .

[8]  Adnan Sözen,et al.  Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data , 2004 .

[9]  Ali Assi,et al.  Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City – UAE , 2011 .

[10]  F. S. Tymvios,et al.  Comparative study of Ångström's and artificial neural networks' methodologies in estimating global solar radiation , 2005 .

[11]  Saleh M. Al-Alawi,et al.  An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation , 1998 .

[12]  Kazuyuki Murase,et al.  A new wrapper feature selection approach using neural network , 2010, Neurocomputing.

[13]  M. Rofiqul Islam,et al.  Renewable energy resources and technologies practice in Bangladesh , 2008 .

[14]  M. Iqbal.  An introduction to solar radiation , 1983 .