Artificial Neural Networks for Predicting Global Solar Radiation in Al Ain City - Uae

Abstract

The geographical location (latitude: 24 • 16 N and longitude: 55 • 36 E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and correlation coefficient (R 2 ) are presented for the model. The values of RMSE, MBE, MAPE, and R 2 are found to be, respectively, 35%, 0.307%, 3.88%, and 92%. A comparison of estimated MGSR with regression models is carried out. The ANN model predicts better than other models. The estimated MGSR data are in reasonable agreement with the actual values. The results indicate the capability of the ANN technique over unseen data and its ability to produce accurate prediction models.

Satellite Remote Sensing for Identification of Solar Potential Sites in Pakistan

“…Solar energy is one of the cheap, pollution free, inexhaustible renewable energy resource [6,14,15,16]. Long term detailed knowledge of potential sites is required for the development of this energy resource, in order to establish the solar houses at a large scale.…”

“…Pakistan is blessed with huge spectrum of al renewable energy sources with high potential including conventional as well as non-conventional which have not been sufficiently explored, employed or developed. Out of range of renewable energy resources, in coming years one of the cleanest, powerful, cheap, safe and virtually inexhaustible energy source upon which the world can depend is solar energy [6,11,12,13,14,15,16]. Global potential of solar energy is 3,000,000 TWh/y [17].…”

Using Artificial Neural Networks for Prediction of Global Solar Radiation in Tehran Considering Particulate Matter Air Pollution

“…Using Multilayer Perceptron and Radial Basis Function neural networks with different input parameters, Maitha et al have predicted monthly solar radiation. In this study, they have demonstrated that Radial Basis Function neural network with Absolute Fraction of Variance of 92% gives a better performance in comparison with Multilayer Perceptron neural network [19].…”

Using measured daily meteorological parameters to predict daily solar radiation

“…Among the empirical methods, machine learning has been widely used to solve real world problems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Artificial neural networks (ANNs) are well-known machine learning systems that have been utilized to predict the solar radiation [2][3][4][29][30][31][32][33][34][35][36][37][38][39][40]. As typical examples in this context, Rahimikhoob [41] successfully applied ANN for predicting the global solar radiation.…”