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Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models. Presenter : Fen-Rou Ciou Authors : Hamdy K. Elminir , Yosry A. Azzam , Farag I. Younes 2007,ENERGY. Outlines. Motivation Objectives Methodology Experiments

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Outlines

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  1. Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models Presenter : Fen-Rou CiouAuthors : Hamdy K. Elminir, Yosry A. Azzam, Farag I. Younes2007,ENERGY

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. • In case that it exists, the quality of these records is not as good as it should be for most purposes and so an estimate of its values is desirable.

  4. Objectives • To achieve such a task, an artificial neural network (ANN) model has been proposed to predict diffuse fraction (KD) in hourly and daily scale.

  5. Methodology

  6. Methodology • It can predict the KD value in hourly and daily scales base on KT and S / S0 • In hourly scale, the input layer has five individual inputs. • In daily scale, the input layer has three input.

  7. Experiments– In hourly scale

  8. Experiments– In hourly scale

  9. Experiments – In daily scale Gopinathan and Solar’s Model ANN’s Model

  10. Experiments – In daily scale Gopinathan and Solar’s Model ANN’s Model

  11. Conclusions • These findings show that the neural network is more suitable to predict diffuse fraction than the proposed regression models at least for the Egyptian sites.

  12. Comments • Advantages • Let me relearn statistics. • Applications • Prediction diffuse fraction

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