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

Bharath Chandra Mummadisetty, Astha Puri, Ershad Sharifahmadian, Shahram Latifi

Department of Electrical & Computer Engineering, University of Nevada, Las Vegas, NV, USA.

DOI: 10.4236/ijcns.2015.86022                

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.

Keywords

Data Compression, Predictive Analysis, Artificial Neural Network, Compression Ratio, Machine Learning, Climate Data Prediction

Conflicts of Interest

The authors declare no conflicts of interest.

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