Solar radiation prediction based on recurrent neural networks trained by Levenberg-Marquardt backpropagation learning algorithm

In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develo...

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Vydáno v:2012 IEEE PES Innovative Smart Grid Technologies s. 1 - 7
Hlavní autoři: Nian Zhang, Behera, P. K.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.01.2012
Témata:
backpropagation learning algorithm
Correlation
Educational institutions
neural networks
Predictive models
Solar energy
Solar radiation
Solar radiation prediction
Time series analysis
time series prediction
Training
ISBN:9781457721588, 1457721589
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Shrnutí:In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient stand-alone photovoltaic systems as well as hybrid power systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the proposed neural network structure and the learning algorithm could be very useful in training the recurrent neural network for the solar radiation prediction.
ISBN:9781457721588
1457721589
DOI:10.1109/ISGT.2012.6175757