forecasting of preprocessed daily solar radiation time series using neural networks
DESCRIPTION
Presenter : Cheng-Han Tsai Authors : Christophe Paoli, Cyril Voyant, Marc Muselli, Marie-Laure Nivet SOLAR ENERGY, 2010. Forecasting of preprocessed daily solar radiation time series using neural networks. Outlines. Motivation Objectives Methodology Experiments Conclusions - PowerPoint PPT PresentationTRANSCRIPT
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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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![Page 2: Forecasting of preprocessed daily solar radiation time series using neural networks](https://reader035.vdocuments.us/reader035/viewer/2022062815/568168e3550346895ddfde9e/html5/thumbnails/2.jpg)
Outlines
• Motivation• Objectives• Methodology• Experiments• Conclusions• Comments
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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 Bayesian Markov chains KNN
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Methodology
ARIMA Bayesian Markov chains KNN
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Methodology
ARIMA Bayesian Markov chains KNN
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Methodology
ARIMA Bayesian Markov chains KNN
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Experiments
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Experiments
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Cleaning the measure errors
Ad-hoc time series preprocessing
Corrected time series
Forecasting methods & Predicted irradiation
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Experiments
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Ad-hoc time series preprocessing
Clearness index Clear sky index
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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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