Predicting Global Solar Radiation Using Recurrent Neural Networks and Climatological Parameters

Several meteorological parameters were used for the 
prediction of monthly average daily global solar radiation on 
horizontal using recurrent neural networks (RNNs). Climatological 
data and measures, mainly air temperature, humidity, sunshine 
duration, and wind speed between 1995 and 2007 were used to design 
and validate a feed forward and recurrent neural network based 
prediction systems. In this paper we present our reference system 
based on a feed-forward multilayer perceptron (MLP) as well as the 
proposed approach based on an RNN model. The obtained results 
were promising and comparable to those obtained by other existing 
empirical and neural models. The experimental results showed the 
advantage of RNNs over simple MLPs when we deal with time series 
solar radiation predictions based on daily climatological data.

• Rami El-Hajj Mohamad
• Mahmoud Skafi
• Ali Massoud Haidar

• Recurrent Neural Networks
• Global Solar Radiation
• Multi-layer perceptron
• gradient
• Root Mean Square Error.

[1] A. Assi and M. Jama, "Estimating global Solar Radiation on Horizontal
from Sunshine Hours in Abu Dhabi UAE”, Proceeding of the 4th
International Conference on Renewable Energy Sources (RES’10), pp.
101-108, May 2010, Sousse, Tunisia.
[2] A. Assi and M. Al-Shamisi, "Prediction of Monthly Average Daily
Global Solar Radiation in Al Ain City, UAE, Using Artificial Neural
Networks,” in Proceedings of the 25th European Photovoltaic Solar
Energy Conference, pp. 508–512, Valencia, Spain, September 2010.
[3] Al-Alawi, S.M., Al-Hinai,, An ANN-Based Approach for Predicting
Global Solar Radiation in Locations with no Measurements, Renewable
Energy, Vol 14 (1–4), 1998 ,pp.199–204.
[4] E. Falayi , J. Adepitan and A. Rabiu, Empirical Models for the
Correlation of Global Solar Radiation with Meteorological Data for
Iseyin, Nigeria, Physical Sciences, 3 (9), 2008, pp.210-216.
[5] Emad A. Ahmed and M. El-Nouby Adam, Estimate of Global Solar
Radiation by Using Artificial Neural Network in Qena, Upper Egypt,
Journal of Clean Energy Technologies, Vol. 1, No. 2, April 2013.
[6] T. Khatib, A. Mohamed, M. Mahmoud, K. Sopian, Estimating Global
Solar Energy Using Multilayer Perception Artificial Neural Network,
International Journal of Energy, Issue 1, Vol. 6, 2012.
[7] Jiang Y. Computation of Monthly Mean Daily Global Solar Radiation in
China Using Artificial Neural Networks and Comparison with Other
Empirical Models. Energy 2009; 34(9).
[8] S. Haykin, Neural Networks and Learning Machines, 2009, 3rd Edition,
Pearson Education, Inc., New Jersey.
[9] P. Stagge and B. Senho. An Extended Elman Net for Modelling Time
Series. In International Conference on Artificial Neural Networks, 1997.
[10] I.M. Galvan and P. Isasi. Multi-Step Learning Rule for Recurrent Neural
Models: An Application to Time Series Forecasting. Neural Processing
Letters, (13):115{133, 2001.
[11] M.I. Jordan. Attractor Dynamics and Parallelism in a Connectionist
Sequential Machine. In Proc. of the Eighth Annual Conference of the
Cognitive Science Society, Pages 531-546. NJ: Erlbaum, 1986.
[12] M.I. Jordan. Serial order: A Parallel Distributed Processing Approach.
Technical Report, Institute for Cognitive Science. University of
California, 1986.