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Search: subject:"data mining algorithms"
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Business process management
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Control theory
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Data Mining
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Data mining
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In this paper
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Industrial Engineering
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Kontrolltheorie
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Production control
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and the integrated prediction model
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and the maximum forecast length of the long-term prediction model is 84 h. The wind farm power prediction models are built with five different data mining algorithms. The accuracy of the generated models is analysed. The model generated by a neural network outperforms all other models for both short- and long-term prediction. Two basic prediction methods are presented: the direct prediction model
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and then the power is generated with the predicted wind speed. The direct prediction model offers better prediction performance than the integrated prediction model. The main source of the prediction error appears to be contributed by the weather forecasting data. 2008 John Wiley Sons
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autocorrelated processes
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cascade processes
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data mining algorithms
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model-based control chart
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models for short- and long-term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short-term prediction model is 12 h
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statistical process control
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whereby the power prediction is generated directly from the weather forecasting data
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whereby the prediction of wind speed is generated with the weather data
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Chen, Victoria C. P.
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Jitpitaklert, Weerawat
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Kim, Seoung Bum
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Kusiak, Andrew
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Lee, Jinpyo
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Park, Sun-Kyoung
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Song, Zhe
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Zheng, Haiyang
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European journal of industrial engineering : EJIE
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Wind farm power prediction: a data-mining approach
Kusiak, Andrew
;
Zheng, Haiyang
;
Song, Zhe
-
2009
prediction models are built with five different
data
mining
algorithms
. The accuracy of the generated models is analysed. The …
Persistent link: https://www.econbiz.de/10009466082
Saved in:
2
Data mining adjustment control charts for cascade processes
Kim, Seoung Bum
;
Jitpitaklert, Weerawat
;
Chen, Victoria …
- In:
European journal of industrial engineering : EJIE
7
(
2013
)
4
,
pp. 442-455
Persistent link: https://www.econbiz.de/10010200304
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