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dc.contributor.authorCirera Balcells, Josep
dc.contributor.authorQuiles Zaguirre, Maria
dc.contributor.authorCariño Corrales, Jesús Adolfo
dc.contributor.authorZurita Millán, Daniel
dc.contributor.authorOrtega Redondo, Juan Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Expressió Gràfica a l'Enginyeria
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.identifier.citationCirera, J., Quiles, M., Cariño, J. A., Zurita, D., Ortega, J.A. Data-driven operation performance evaluation of multi-chiller system using self-organizing maps. A: IEEE International Conference on Industrial Technology. "2018 IEEE International Conference on Industrial Technology (ICIT): Lyon, France: February 19-22, 2018: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 2099-2104.
dc.description© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.description.abstractIndustrial plants performance evaluation has become a difficult task due to the machinery complexity. Multi-chiller systems take up big proportion of energy in food and beverage companies. Complex refrigeration generation is usually hard to evaluate as the affectation of external signals plays an important role and also exist too many control features for the facility operator. Develop a method able to detect any deviation respect the optimal operation can provide the necessary information for the purpose of inefficiencies identification and a further optimization. In this paper, data-driven methods are used in order to describe a reliable coefficient of performance indicator (COP) in several known plant conditions. Self-organizing maps (SOM) are used to recognize different operating points among the multi-variable feature space for later performance evaluation. By the analysis of COP in each operating point, the potential energy saving can be illustrated. An experimental study is performed with refrigeration plant indicating the suitability of the proposed method
dc.format.extent6 p.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshArtificial intelligence
dc.subject.lcshMachine learning
dc.subject.lcshNeural networks (Computer science)--Industrial applications
dc.subject.lcshSelf-organizing maps--Industrial applications
dc.subject.otherArtificial intelligence
dc.subject.otherCondition monitoring
dc.subject.otherMachine learning
dc.subject.otherMultidimensional systems
dc.subject.otherNeural networks
dc.subject.otherSelf-organizing feature maps
dc.subject.otherUnsupervised learning
dc.subject.otherSelf-organizing feature maps
dc.subject.otherPower measurement
dc.subject.otherQ measurement
dc.subject.otherPressure measurement
dc.subject.otherTemperature measurement
dc.subject.otherPrincipal component analysis
dc.titleData-driven operation performance evaluation of multi-chiller system using self-organizing maps
dc.typeConference report
dc.subject.lemacIntel·ligència artificial -- Aplicacions industrials
dc.subject.lemacMaquinària -- Monitoratge
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacXarxes neuronals (Informàtica) -- Aplicacions industrials
dc.subject.lemacSistemes autoorganitzatius
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (author's final draft)
local.citation.authorCirera, J.; Quiles, M.; Cariño, J. A.; Zurita, D.; Ortega, J.A.
local.citation.contributorIEEE International Conference on Industrial Technology
local.citation.publicationName2018 IEEE International Conference on Industrial Technology (ICIT): Lyon, France: February 19-22, 2018: proceedings

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