TITLE:
A Hybrid Method for Compression of Solar Radiation Data Using Neural Networks
AUTHORS:
Bharath Chandra Mummadisetty, Astha Puri, Ershad Sharifahmadian, Shahram Latifi
KEYWORDS:
Data Compression, Predictive Analysis, Artificial Neural Network, Compression Ratio, Machine Learning, Climate Data Prediction
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.8 No.6,
June
24,
2015
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.