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A Hybrid Method for Compression of Solar Radiation Data Using Neural Networks

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DOI: 10.4236/ijcns.2015.86022    2,502 Downloads   2,979 Views   Citations

ABSTRACT

The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Mummadisetty, B. , Puri, A. , Sharifahmadian, E. and Latifi, S. (2015) A Hybrid Method for Compression of Solar Radiation Data Using Neural Networks. International Journal of Communications, Network and System Sciences, 8, 217-228. doi: 10.4236/ijcns.2015.86022.

References

[1] Khatib, T., Mohamed, A., Sopian, K. and Mahmoud, M. (2012) Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction. International Journal of Photoenergy, 2012, Article ID: 946890.
http://dx.doi.org/10.1155/2012/946890
[2] 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 Sust-ainable Energy Reviews, 31, 509-519. http://dx.doi.org/10.1016/j.rser.2013.12.008
[3] Yadav, A.K. and Chandel, S.S. (2014) Solar Radiation Prediction Using Artificial Neural Network Techniques: A Review. Renewable and Sustainable Energy Reviews, 33, 772-781.
http://dx.doi.org/10.1016/j.rser.2013.08.055
[4] Al-Alawi, S.M. and Al-Hinai, H.A. (1998) An ANN-Based Approach for Predicting Global Radiation in Locations with No Direct Measurement Instrumentation. Renewable Energy, 14, 199-204.
http://dx.doi.org/10.1016/S0960-1481(98)00068-8
[5] Sözen, A., Arcaklioglu, E., Özalp, M. and Kanit, E.G. (2004) Use of Artificial Neural Networks for Mapping of Solar Potential in Turkey. Applied Energy, 77, 273-286.
http://dx.doi.org/10.1016/S0306-2619(03)00137-5
[6] Sözen, A., Arcaklioglu, E. and Özalp, M. (2004) Estimation of Solar Potential in Turkey by Artificial Neural Networks Using Meteorological and Geographical Data. Energy Conversion and Management, 45, 3033-3052.
[7] AbdAlKader, S.A. and AL-Allaf, N.A. (2011) Backpropagation Neural Network Algorithm for Forecasting Soil Temperatures Considering Many Aspects: A Comparison of Different Approaches. The 5th International Conference on Information Technology, Amman, 11-13 May 2011, 11-13.
[8] Mummadisetty, B.C., Puri, A., Sharifahmadian, E. and Latifi, S. (2014) Lossless Compression of Climate Data. Proceedings of the 23rd International Conference on Systems Engineering, Vol. 330, 391-400.
[9] Xu, K., Song, J.D., Zhao, Y.W., Bi, Q. and Liu, Y. (2013) Forecasting Model Based on an Improved Elman Neural Network and Its Application in the Agricultural Production. IEEE International Conference on Granular Computing, Beijing, 13-15 December 2013, 202-207.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6740408
[10] Rosenblatt, F. (1962) Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC. http://catalog.hathitrust.org/Record/000203591
[11] Basic MLP Neural Network Model.
http://en.wikibooks.org/wiki/Artificial_Neural_Networks/Activation_Functions
[12] Cascade Feed Forward Neural Network.
http://www.cs.cornell.edu/boom/2004sp/projectarch/appofneuralnetworkcrystallography/NeuralNet workCascadeCorrelation.htm
[13] Paraskevas, T., Dimitrios, R. and Andreas, B. (2014) Use of Artificial Neural Network for Spatial Rainfall Analysis. Journal of Earth System Science, 123, 457-465.
http://dx.doi.org/10.1007/s12040-014-0417-0
[14] Al Shamisi, M.H., Assi, A.H. and Hejase, H.A. (2011) Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City—UAE. Engineering Education and Research Using MATLAB.
http://www.intechopen.com/books/engineering-education-and-research-using-matlab/using-matlab -to-develop-artificial-neural-network-models-for-predicting-global-solar-radiation-in-al
[15] Representation of Elman Neural Network. http://mnemstudio.org/neural-networks-elman.htm
[16] Lina, R., Liu, Y.X., Rui, Z.Y., Li, H.Y. and Feng, R.C. (2009) Application of Elman Neural Network and MATLAB to Load Forecasting. Proceedings of the International Conference on Information Technology and Computer Science, Kiev, 25-26 July 2009, 55-59.

  
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