Forecasting of preprocessed daily
solar radiation time series using
neural networks
Presenter : Cheng-Han Tsai
Authors : Christophe Paoli, Cyril Voyant, Marc Muselli, Marie-Laure
Nivet
SOLAR ENERGY, 2010
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Outlines
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Motivation
Objectives
Methodology
Experiments
Conclusions
Comments
Motivation
• A lot of methods’ performance be affected by
disruptors such as diffuse, ground-reflected
and seasonal climate.
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Objectives
• This paper has used a MLP and pre-processing
for the daily prediction of global solar
radiation to deal with the above problems.
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Methodology
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Methodology
ARIMA
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Bayesian
Markov
chains
KNN
Methodology
ARIMA
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Bayesian
Markov
chains
KNN
Methodology
ARIMA
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Bayesian
Markov
chains
KNN
Methodology
ARIMA
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Bayesian
Markov
chains
KNN
Experiments
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Experiments
Cleaning the measure
errors
Ad-hoc time series
preprocessing
Corrected time series
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Forecasting methods &
Predicted irradiation
Experiments
Ad-hoc time series
preprocessing
Clearness index
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Clear sky index
Experiments
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Experiments
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Experiments
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Conclusions
• This prediction model has been compared to
other prediction methods
• These simulation tools have been successfully
validated on the DC energy prediction
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Comments
• Advantages
– This paper considers seasonal factors
• Applications
– Solar radiation prediction
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