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dc.contributor.authorKampouropoulos, Konstantinos
dc.contributor.authorCárdenas Araújo, Juan José
dc.contributor.authorGiacometto Torres, Francisco
dc.contributor.authorRomeral Martínez, José Luis
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2017-09-08T10:21:57Z
dc.date.issued2013
dc.identifier.citationKampouropoulos, K., Cardenas, J., Giacometto, Francisco javier, Romeral, L. An energy prediction method using adaptive Neuro-Fuzzy Inference System and Genetic Algorithms. A: IEEE International Symposium on Industrial Electronics. "ISIE 2013 : proceedings of the IEEE 22nd International Symposium on Industrial Electronics". Taipei: 2013, p. 1-6.
dc.identifier.isbn978-1-4673-5194-2
dc.identifier.urihttp://hdl.handle.net/2117/107522
dc.description.abstractThis document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.
dc.format.extent6 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.lcshVector fields
dc.subject.lcshAlgorithms
dc.subject.lcshForce and energy
dc.subject.lcshForecasting
dc.subject.otherIntelligent Energy Management Systems
dc.subject.otherEnergy forecast
dc.subject.otherAdaptive Neuro-Fuzzy Inference System
dc.subject.otherGenetic Algorithm
dc.subject.otherTraining
dc.subject.otherGenetic algorithms
dc.subject.otherBiological cells
dc.subject.otherSociology
dc.subject.otherStatistics
dc.subject.otherMathematical model
dc.subject.otherVectors
dc.titleAn energy prediction method using adaptive Neuro-Fuzzy Inference System and Genetic Algorithms
dc.typeConference report
dc.subject.lemacCamps vectorials
dc.subject.lemacAlgorismes
dc.subject.lemacEnergia
dc.subject.lemacPrevisió
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ISIE.2013.6563627
dc.relation.publisherversionhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6563627&isnumber=6563588
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac21182947
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorKampouropoulos, K.; Cardenas, J.; Giacometto, Francisco javier; Romeral, L.
local.citation.contributorIEEE International Symposium on Industrial Electronics
local.citation.pubplaceTaipei
local.citation.publicationNameISIE 2013 : proceedings of the IEEE 22nd International Symposium on Industrial Electronics
local.citation.startingPage1
local.citation.endingPage6


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