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~person:"Kusiak, Andrew"
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Industrial Engineering
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Sustainability
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In this paper
1
Ltd
1
Models and algorithms for effective decision-making in a data-driven environment are discussed. To enhance the quality of the extracted knowledge and decision-making
1
The IDEF methodology has been extensively used for modeling various processes. Qualitative and quantitative reliability analysis and risk assessment of IDEF models is of interest to industry for several reasons. It identifies critical activities in a process
1
a subset of rules is selected from the extracted knowledge to meet the established decision-making criteria. The parameter values represented by the conditions of this set of rules are called a decision signature. A model and algorithms for the selection of the desired parameters (decision signatures) will be developed. The parameters of this model are updated using a framework provided by the learning classifier systems
1
and decreases downtime and operating cost of the process. To evaluate the risk associated with an IDEF3 model formal tools and techniques are required. In this paper
1
and the integrated prediction model
1
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
1
and the parameters optimizing process performance are recommended. The applications discussed in this paper differ from most data mining tasks
1
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
1
improves the process performance
1
in a typical data mining application the equipment fault is recognized based on the failure symptoms. In this paper
1
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
1
the data sets are transformed
1
the fault tree analysis technique and minimum cut and path sets generation algorithms are applied for reliability evaluation and risk assessment of the parent activities in an IDEF3 model. A structural and reliability importance measure for parent activities in an IDEF3 model as well as for the elementary activities in a decomposed model are presented
1
the impact of the decisions on the modeled process is simulated
1
the knowledge is extracted with multiple algorithms
1
where the extracted knowledge is used to assign decision values to new objects that have not been included in the training data. For example
1
whereby the power prediction is generated directly from the weather forecasting data
1
whereby the prediction of wind speed is generated with the weather data
1
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Kusiak, Andrew
Bolinger, Mark
9
Svejnar, Jan
9
Wiser, Ryan
8
Beyerlein, Michael
5
Barbose, Galen
4
Commander, Simon
4
Goldman, Charles
4
Marnay, Chris
4
Ariff, Mohamed
3
Boak, D.M.
3
Bretherton, P
3
Chen, Wenjie
3
Denholm, P.
3
Goldman, Charles A.
3
Hanousek, Jan
3
Hopper, Nicole C.
3
Kocenda, Evžen
3
Osborn, Julie G.
3
Prindle, N.H.
3
Prybutok, Victor R.
3
Ronald L. Boring
3
Siddiqui, Afzal S.
3
Alves, Pana
2
Angelucci, Manuela
2
Artzner, Denis
2
Ballentine, Rodger
2
Borison, A.
2
Brouthers, Keith D.
2
Brouthers, Lance
2
Chari, Anusha
2
Craig, Justin B.
2
David I. Gertman
2
Davis, Peter S.
2
Delgado Rodríguez, Francisco Javier
2
Dibrell, Clay
2
Dominguez, Kathryn M. E.
2
Drury, E.
2
Estrin, Saul
2
Ferris, Stephen P.
2
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1
Wind farm power prediction: a data-mining approach
Kusiak, Andrew
;
Zheng, Haiyang
;
Song, Zhe
-
2009
prediction
performance
than the integrated prediction model. The main source of the prediction error appears to be contributed by …
Persistent link: https://www.econbiz.de/10009466082
Saved in:
2
Data mining and decision making
Kusiak, Andrew
-
2002
algorithms, the impact of the decisions on the modeled process is simulated, and the parameters optimizing process
performance
…
Persistent link: https://www.econbiz.de/10009466041
Saved in:
3
Risk assessment of process models
Kusiak, Andrew
;
Zakarian, Armen
-
1996
in a process, improves the process
performance
, and decreases downtime and operating cost of the process. To evaluate the …
Persistent link: https://www.econbiz.de/10009466081
Saved in:
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