Documents disponibles écrits par cet auteur

Clustering-based performance optimization of the boiler–turbine system / Kusiak, A. in IEEE transactions on energy conversion, Vol. 23 n°2 (Juin 2008) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 651 - 658 Titre : Clustering-based performance optimization of the boiler–turbine system Type de document : texte imprimé Auteurs : Kusiak, A., Auteur ; Zhe, Song, Auteur Année de publication : 2008 Article en page(s) : pp. 651 - 658 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Boilers;  data  mining;  power  engineering  computing;  turbines Résumé : In this paper, two optimization models for improvement of the boiler-turbine system performance are formulated. The models are constructed using a data-mining approach. Historical process data is clustered and the discovered patterns are selected for performance improvement of the boiler-turbine system. The first model optimizes a widely used performance index, the unit heat rate. The second model minimizes the total fuel consumption while meeting the electricity demand. The strengths and weaknesses of the two models are discussed. An industrial case study illustrates the concepts presented in the paper. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4456516&sortType%3Das [...] [article] Clustering-based performance optimization of the boiler–turbine system [texte imprimé] / Kusiak, A., Auteur ; Zhe, Song, Auteur . - 2008 . - pp. 651 - 658. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 651 - 658 Mots-clés : Boilers;  data  mining;  power  engineering  computing;  turbines Résumé : In this paper, two optimization models for improvement of the boiler-turbine system performance are formulated. The models are constructed using a data-mining approach. Historical process data is clustered and the discovered patterns are selected for performance improvement of the boiler-turbine system. The first model optimizes a widely used performance index, the unit heat rate. The second model minimizes the total fuel consumption while meeting the electricity demand. The strengths and weaknesses of the two models are discussed. An industrial case study illustrates the concepts presented in the paper. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4456516&sortType%3Das [...]

Short-horizon prediction of wind power / Kusiak, A. in IEEE transactions on energy conversion, Vol. 25, N° 4 (Décembre 2010) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 25, N° 4 (Décembre 2010) . - pp. 1112 - 1122 Titre : Short-horizon prediction of wind power : a data-driven approach Type de document : texte imprimé Auteurs : Kusiak, A., Auteur ; Zijun Zhang, Auteur Année de publication : 2011 Article en page(s) : pp. 1112 - 1122 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Data  mining;  neural  nets;  power  engineering  computing;  prediction  theory;  statistical  analysis;  wind  power  plants Résumé : This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5451084&sortType%3Das [...] [article] Short-horizon prediction of wind power : a data-driven approach [texte imprimé] / Kusiak, A., Auteur ; Zijun Zhang, Auteur . - 2011 . - pp. 1112 - 1122. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 25, N° 4 (Décembre 2010) . - pp. 1112 - 1122 Mots-clés : Data  mining;  neural  nets;  power  engineering  computing;  prediction  theory;  statistical  analysis;  wind  power  plants Résumé : This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5451084&sortType%3Das [...]

Short-term prediction of wind farm power / Kusiak, A. in IEEE transactions on energy conversion, Vol. 24 N°1 (Mars 2009) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 24 N°1 (Mars 2009) . - pp. 125 - 136 Titre : Short-term prediction of wind farm power : a data mining approach Type de document : texte imprimé Auteurs : Kusiak, A., Auteur ; Haiyang, Zheng, Auteur ; Zhe, Song, Auteur Année de publication : 2009 Article en page(s) : pp. 125 - 136 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Data  mining;  power  system  analysis  computing;  wind  power  plants Résumé : This paper examines time series models for predicting the power of a wind farm at different time scales, i.e., 10-min and hour-long intervals. The time series models are built with data mining algorithms. Five different data mining algorithms have been tested on various wind farm datasets. Two of the five algorithms performed particularly well. The support vector machine regression algorithm provides accurate predictions of wind power and wind speed at 10-min intervals up to 1 h into the future, while the multilayer perceptron algorithm is accurate in predicting power over hour-long intervals up to 4 h ahead. Wind speed can be predicted fairly accurately based on its historical values; however, the power cannot be accurately determined given a power curve model and the predicted wind speed. Test computational results of all time series models and data mining algorithms are discussed. The tests were performed on data generated at a wind farm of 100 turbines. Suggestions for future research are provided. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4749292&sortType%3Das [...] [article] Short-term prediction of wind farm power : a data mining approach [texte imprimé] / Kusiak, A., Auteur ; Haiyang, Zheng, Auteur ; Zhe, Song, Auteur . - 2009 . - pp. 125 - 136. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 24 N°1 (Mars 2009) . - pp. 125 - 136 Mots-clés : Data  mining;  power  system  analysis  computing;  wind  power  plants Résumé : This paper examines time series models for predicting the power of a wind farm at different time scales, i.e., 10-min and hour-long intervals. The time series models are built with data mining algorithms. Five different data mining algorithms have been tested on various wind farm datasets. Two of the five algorithms performed particularly well. The support vector machine regression algorithm provides accurate predictions of wind power and wind speed at 10-min intervals up to 1 h into the future, while the multilayer perceptron algorithm is accurate in predicting power over hour-long intervals up to 4 h ahead. Wind speed can be predicted fairly accurately based on its historical values; however, the power cannot be accurately determined given a power curve model and the predicted wind speed. Test computational results of all time series models and data mining algorithms are discussed. The tests were performed on data generated at a wind farm of 100 turbines. Suggestions for future research are provided. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4749292&sortType%3Das [...]