Performance evaluation of Neural Network and Multivariate Regression Methods for Estimation of Total Solar Radiation at several stations in Arid and Semi-arid Climates

Document Type : Research Paper


1 1. M. Sc. Student in Water Resources Engineering and member of Young Researchers Society, Water Engineering Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman,


In this study, the capability of multi-layer perceptron (MLP) and multivariate linear regression methods were evaluated to estimate the total solar radiation. For this purpose, the daily weather data of 25 years (1992-2017) including maximum temperature, mean temperature, relative humidity, sunshine hours and solar radiation were used in the five synoptic stations (Bandarabbas, Zanjan, Shiraz, Kerman and Mashhad). The inputs used in the models included various combinations of these variables, and the output was the solar radiation. To evaluate the performance of these models, Determination of Coefficient (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Index of Agreement (IA) were used. In order to train the structure of the ANN, two Bayesian-regularization (Br) and Levenberg-Marquardt (LM) algorithms were compared. Moreover, the training and validation processes were performed. The results of regression model showed that all the input variables are effective on the solar radiation estimation at Bandarabbas, Zanjan and Shiraz, but the effect of relative humidity on radiation at Kerman and Mashhad stations was low. The ANN application with two algorithms showed that Bandarabbas and Kerman stations using the Br algorithm and Zanjan, Shiraz and Mashhad using the LM algorithm give a good result. The lowest values of RMSE, MAE and the highest value of IA and R2 related to Kerman station were 2.799, 0.94, 0.954 and 0.838, respectively. As a main result, the comparison between computation and observation data showed that the ANN model gives better results than the linear regression model for estimation of radiation.


Main Subjects

AL-Fatlawi, A. W. A., Qazi, A., Hussain, F. and Ahmed Khan, W. (2015). The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. Journal of Cleaner Production, 104, 1-12.
Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.
Agha Shariatmadari, Z., Khalili, A., irannezhad, P. and Leyaghat, A. (2012). Calibration and annual variations of Angstrom-Prescott's (a and b) coefficients in different time scales (Case study: Tehran-Shahr Station (Aghdasiyeh). Journal of Water and Soil, 25 (4), 905-911. (In Farsi)
Bayat, k. and Mirlatifi, S. M. (2010). Estimation of daily total solar radiation using artificial neural networks and comparing it with experimental methods at three stations in Shiraz, Karaj and Ramsar. Journal of Water and Soil Science, Science and Technology of Agriculture and Natural Resources- Isfahan University of Technology, 16 (61), 1-13. (In Farsi)
Benghanem, M., Mellit, A. and Alamri, S. N. (2009). ANN-based modelling and estimation of daily global solar radiation data: Acase study. Energy conversion and management, 50(7), 1644-1655.
DehghaniSanij, H., Yamamoto, T. and Rasiah, V. (2004). Assessment of evapotranspiration estimation models for use in semi-arid environments. Agricultural water management, 64(2), 91-106.
Ghabaei Sough, M., Mosaedi, A. and Dehghani, A. A. (2012). Solar radiation data and their intelligent modeling based on gamma test with evaluation of calibrated empirical equations. Jornal of Water and Soil Conservation, 18(4), 158-208. (In Farsi)
Ghahreman, N. and Bakhtiari, B. (2009). Solar radiation estimation from rainfall and temperature data in arid and semi-arid climates of Iran. DESERT, 14(2), 141-150.
Hecht-Nielsen, R. (1987). Kolmogorov's mapping neural network existence theorem. In Proceedings of the IEEE International Conference on Neural Networks III, IEEE Press. 3, 11-14.
Izadifar, Z. and Elshorbagy, A. (2010). Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models. Hydrological Processes,24, 3413-3425.
Jang, J. and Roger, S. (2003). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Jensen, M. E. and Haise, H. R. (1963). Estimating evapotranspiration from solar radiation. Journal of the Irrigation and Drainage Division, Proceedings of the American Society of Civil Engineers, 89, 15-41.
Jimenez, A. V., Barrionuevo, A., Will, A. and Rodriguez, S. (2016). Neural network for estimating daily global solar radiation using temperature, humidity and pressure as unique climatic input variables. Smart Grid and Renewable Energy, 7(3), 94-103.
Kumar, R., Aggarwal, R. K. and Sharma, J. D. (2015). Comparison of regression and artificial neural network models for estimation of global solar radiations. Renewable and Sustainable Energy Reviews, 52, 1294-1299.
Legates D. R. and Mc Cabe Jr. G. J. (1999). Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water and Resources Research, 35(1), 233-241.
Makkink, G. F. (1957). Testing the Penman formula by means of lysimeters. Journal of the Institution of Water Engineerrs, 11(3), 277–288.
Menhaj, M. B. (2006). Fundamentals of Neural Networks. Amirkabir University of Technology. Tehran, 716 P.
Moradi, I. (2009). Quality control of global solar radiation using sunshine duration hours. Energy, 34(1), 1-6.
Nait Mensour, O., El Ghazzani, B., Hlimi, B. and Ihlal, A. (2017). Modeling of solar energy potential in Souss-Massa area-Morocco, using intelligence Artificial Neural Networks (ANNs). Energy Procedia, 139, 778-784.
Olalekan, S., Abdullahi, M. I. and Olabisi, A. (2018). Modeling of Solar Radiation Using Artificial Neural Network for Renewable Energy Application. Journal of Applied Physics, 10(2), 6-12.
Ramedani, Z., Omid, M. and Keyhani, A. (2013). Modeling solar energy potential in a Tehran province using artificial neural networks. International Journal of Green Energy, 10(4), 427-441
Rahimi, J., Ebrahimpour, M. and Khalili, A. (2013). Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theoretical and applied climatology, 112(3-4), 409-418.
Razi, M. A. and Athappilly, K. (2005). A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Systems with Applications, 29(1), 65-74.
Rezaei, A. and Meybodi, A. (2006). Statistics and Probability. Isfahan University of Technology, 590 P. (In Farsi)
Saffari Pour, M. H. and Mehrabian, M. A. (2010). Estimation of the total amount of solar radiation in Kerman using geometric, astronomical, geographic and meteorological characteristics. Sharif Scientific and Research Magazine, 51, 3-13. (In Farsi)
Tabari, H., Grismer, M. E. and Trajkovic, S. (2013). Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science, 31(2), 107-117.
Turc, L. (1961). Evaluation des besoins en eau d'irrigation, évapotranspiration potentielle. Annual Agronomy, 12, 13-49.
Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63(11), 1309-1313.
Yadav, A. K. and Chandel, S. S. (2015). Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model. Renewable Energy, 75, 675-693.
Yadav, A. K., Malik, H. and Chandel, S. S. (2014). Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renewable and Sustainable Energy Reviews, 31, 509-519.
Yan, X., Abbes, D. and Francois, B. (2014). Solar radiation forecasting using Artificial Neural Network for local power reserve. International Conference on Electrical Sciences and Technologies in Maghreb, (CISTEM), Tunis, Tunisia, 1-6.
Yang, H., Griffiths, P. R. and Tate, J. D. (2003). Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra. Analytica Chimica Acta, 489(2), 125-136.