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.
Highlights
The earth radiation consists of the energy entering the surface, absorbed, emitted, and reflected by earth system
The above mentioned Artificial Neural Networks (ANN) models were trained with the solar radiation data obtained from the Nevada Climate Change Portal for the year 2012
In this paper we have presented a hybrid method for compression of solar radiation data for the year 2013
Summary
The earth radiation consists of the energy entering the surface, absorbed, emitted, and reflected by earth system. The information about potential for solar energy at a particular location on earth can be provided using solar radiation data during a specific period of time. The prediction of solar radiation has generated a renewed interest in recent years, mostly due to its relevance in renewable energy research and applications. Visible, and a limited portion of infrared energy (together sometimes called “shortwave radiation”) from the sun drives the earth’s climate system Some of this incoming radiation is reflected off clouds, some is absorbed by the atmosphere, and some passes through to the earth’s surface.
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