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dc.contributor.authorMendia, Izaskun
dc.contributor.authorGil-Lopez, Sergio
dc.contributor.authorDel Ser, Javier
dc.contributor.authorGrau, Iñaki
dc.contributor.authorLejarazu, Adelaida
dc.contributor.authorMaqueda, Erik
dc.contributor.authorPerea, Eugenio
dc.date.accessioned2020-12-15T10:59:24Z
dc.date.available2020-12-15T10:59:24Z
dc.date.issued2020-10-27
dc.identifier.citationMendia, Izaskun, Sergio Gil-Lopez, Javier Del Ser, Iñaki Grau, Adelaida Lejarazu, Erik Maqueda, and Eugenio Perea. “An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments.” Intelligent Data Engineering and Automated Learning – IDEAL 2020 (2020): 92–103. doi:10.1007/978-3-030-62365-4_9.en
dc.identifier.isbn978-3-030-62365-4; 978-3-030-62364-7en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/11556/1032
dc.description.abstractThe concern of the industrial sector about the increase of energy costs has stimulated the development of new strategies for the effective management of energy consumption in industrial setups. Along with this growth, the irruption and continuous development of digital technologies have generated increasingly complex industrial ecosystems. These ecosystems are supported by a large number of variables and procedures for the operation and control of industrial processes and assets. This heterogeneous technological scenario has made industries difficult to manage by traditional means. In this context, the disruptive potential of cyber physical systems is beginning to be considered in the automation and improvement of industrial services. Particularly, intelligent data-driven approaches relying on the combination of Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Internet of Things (IoT) and Data Analytics provide the intelligence needed to optimally operate these complex industrial environments. The work presented in this manuscript contributes to the definition of the aforementioned intelligent data-driven approaches, defining a systematic, intelligent procedure for the energy efficiency diagnosis and improvement of industrial plants. This data-based diagnostic procedure hinges on the analysis of data collected from industrial plants, aimed at minimizing energy costs through the continuous assessment of the production-consumption ratio of the plant (i.e. energy per piece or kg produced). The proposed methodology aims to support managers and energy-efficiency technicians to minimize the plant’s energy consumption without affecting the production and therefore, increase its competitiveness. The data used in the design of this methodology are real data from a company dedicated to the design and manufacture of automotive components and one of the main manufacturers in the automotive sector worldwide. The present methodology is under the pending patent application EU19382002.4-120.en
dc.description.sponsorshipThis work has received funding support from the HAZITEK program of the Basque Government (Spain) through the NAIA (Ref. ZL-2017/00701) research grants. It is also appreciate the deference of the company GESTAMP, especially to Iñaki Grau, to provide data from several of its plants. Finally, Javier Del Ser acknowledged funding support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government, as well as by ELKARTEK and EMAITEK programs of this same institution.en
dc.language.isoengen
dc.publisherSpringeren
dc.titleAn Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environmentsen
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-030-62365-4_9en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsEnergy efficiencyen
dc.subject.keywordsSmart manufacturingen
dc.subject.keywordsIntelligent systemsen
dc.subject.keywordsIndustry 4.0en
dc.subject.keywordsBig dataen
dc.subject.keywordsCyber physical systemsen
dc.identifier.essn1611-3349en
dc.journal.titleLecture Notes in Computer Science book seriesen
dc.page.final103en
dc.page.initial92en
dc.volume.number12490en
dc.conference.titleIntelligent Data Engineering and Automated Learning – IDEAL 2020en


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