intelligent database systems lab presenter : fen-rou ciou authors : hamdy k. elminir, yosry a....
DESCRIPTION
Intelligent Database Systems Lab 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.TRANSCRIPT
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Intelligent Database Systems Lab
Presenter : Fen-Rou Ciou
Authors : Hamdy K. Elminir, Yosry A. Azzam, Farag I. Younes
2007,ENERGY
Prediction of hourly and daily diffuse fraction using neural network, as
compared to linear regression models
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Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Intelligent Database Systems Lab
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.
![Page 4: Intelligent Database Systems Lab Presenter : Fen-Rou Ciou Authors : Hamdy K. Elminir, Yosry A. Azzam,…](https://reader035.vdocument.in/reader035/viewer/2022081606/5a4d1bdc7f8b9ab0599dd3cf/html5/thumbnails/4.jpg)
Intelligent Database Systems Lab
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.
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Intelligent Database Systems Lab
Methodology
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Intelligent Database Systems Lab
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.
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Intelligent Database Systems Lab
Experiments– In hourly scale
ANN Erbssandard error(SE) ≌4.18% ≌5.59%
R2 0.95-0.89 0.87-0.65
Correlation cofficient(r)
0.96 0.87
standard deviation(SD) less than 0.2MJ/m2
99.7% 71.7%
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Intelligent Database Systems Lab
Experiments– In hourly scale
Cairo South Valley AswanMBE(MJ/m2)
0.16 – (-0.13)
0.14 – (-0.09)
0.02
RMSE 0.35 0.27 0.23
Correlation cofficient(r)
0.94 0.98 0.99
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Intelligent Database Systems Lab
Experiments – In daily scaleGopinathan and Solar’s Model ANN’s Model
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Intelligent Database Systems Lab
Experiments – In daily scaleGopinathan and Solar’s Model ANN’s Model
GS ANNMBE(MJ/m2)
-0.16 – (0.22)
-0.14 – (0.04)
RMSE 1.14 0.59
SE 5.96-2.51% 17.76-4.84%
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Intelligent Database Systems Lab
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.
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Intelligent Database Systems Lab
Comments• Advantages– Let me relearn statistics.
• Applications– Prediction diffuse fraction