Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/604686
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dc.contributor.authorJun Zhaoen_US
dc.contributor.authorZhongyang Hanen_US
dc.contributor.authorWitold Pedryczen_US
dc.contributor.authorWei Wangen_US
dc.date.accessioned2020-05-20T09:00:50Z-
dc.date.available2020-05-20T09:00:50Z-
dc.date.issued2016en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.other10.1109/TCYB.2015.2445918en_US
dc.identifier.urihttp://localhost/handle/Hannan/136650en_US
dc.identifier.urihttp://localhost/handle/Hannan/604686-
dc.description.abstractSound energy scheduling and allocation is of paramount significance for the current steel industry, and the quantitative prediction of energy media is being regarded as the prerequisite for such challenging tasks. In this paper, a long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes. When forming information granules on a basis of experimental data, we propose to deal with the unequal-length temporal granules by exploiting dynamic time warping, which becomes instrumental to the realization of the prediction model. The model engages the fuzzy C-means clustering method. To quantify the performance of the proposed method, real-world industrial energy data coming from a steel plant in China are employed. The experimental results demonstrate that the proposed method is superior to some other data-driven methods and becomes capable of satisfying the requirements of the practically viable prediction.en_US
dc.publisherIEEEen_US
dc.relation.haspart7151826.pdfen_US
dc.subjectEnergy system|steel industry|long-term prediction|granular computing (GrC)en_US
dc.titleGranular Model of Long-Term Prediction for Energy System in Steel Industryen_US
dc.typeArticleen_US
dc.journal.volume46en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Cyberneticsen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorJun Zhaoen_US
dc.contributor.authorZhongyang Hanen_US
dc.contributor.authorWitold Pedryczen_US
dc.contributor.authorWei Wangen_US
dc.date.accessioned2020-05-20T09:00:50Z-
dc.date.available2020-05-20T09:00:50Z-
dc.date.issued2016en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.other10.1109/TCYB.2015.2445918en_US
dc.identifier.urihttp://localhost/handle/Hannan/136650en_US
dc.identifier.urihttp://localhost/handle/Hannan/604686-
dc.description.abstractSound energy scheduling and allocation is of paramount significance for the current steel industry, and the quantitative prediction of energy media is being regarded as the prerequisite for such challenging tasks. In this paper, a long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes. When forming information granules on a basis of experimental data, we propose to deal with the unequal-length temporal granules by exploiting dynamic time warping, which becomes instrumental to the realization of the prediction model. The model engages the fuzzy C-means clustering method. To quantify the performance of the proposed method, real-world industrial energy data coming from a steel plant in China are employed. The experimental results demonstrate that the proposed method is superior to some other data-driven methods and becomes capable of satisfying the requirements of the practically viable prediction.en_US
dc.publisherIEEEen_US
dc.relation.haspart7151826.pdfen_US
dc.subjectEnergy system|steel industry|long-term prediction|granular computing (GrC)en_US
dc.titleGranular Model of Long-Term Prediction for Energy System in Steel Industryen_US
dc.typeArticleen_US
dc.journal.volume46en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Cyberneticsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7151826.pdf2.2 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJun Zhaoen_US
dc.contributor.authorZhongyang Hanen_US
dc.contributor.authorWitold Pedryczen_US
dc.contributor.authorWei Wangen_US
dc.date.accessioned2020-05-20T09:00:50Z-
dc.date.available2020-05-20T09:00:50Z-
dc.date.issued2016en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.other10.1109/TCYB.2015.2445918en_US
dc.identifier.urihttp://localhost/handle/Hannan/136650en_US
dc.identifier.urihttp://localhost/handle/Hannan/604686-
dc.description.abstractSound energy scheduling and allocation is of paramount significance for the current steel industry, and the quantitative prediction of energy media is being regarded as the prerequisite for such challenging tasks. In this paper, a long-term prediction for the energy flows is proposed by using a granular computing-based method that considers industrial-driven semantics and granulates the initial data based on the specificity of manufacturing processes. When forming information granules on a basis of experimental data, we propose to deal with the unequal-length temporal granules by exploiting dynamic time warping, which becomes instrumental to the realization of the prediction model. The model engages the fuzzy C-means clustering method. To quantify the performance of the proposed method, real-world industrial energy data coming from a steel plant in China are employed. The experimental results demonstrate that the proposed method is superior to some other data-driven methods and becomes capable of satisfying the requirements of the practically viable prediction.en_US
dc.publisherIEEEen_US
dc.relation.haspart7151826.pdfen_US
dc.subjectEnergy system|steel industry|long-term prediction|granular computing (GrC)en_US
dc.titleGranular Model of Long-Term Prediction for Energy System in Steel Industryen_US
dc.typeArticleen_US
dc.journal.volume46en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Cyberneticsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7151826.pdf2.2 MBAdobe PDFThumbnail
Preview File