Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο: https://hdl.handle.net/20.500.14279/17956
Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2020-03-04T08:05:48Z-
dc.date.available2020-03-04T08:05:48Z-
dc.date.issued2006-07-
dc.identifier.citationInternational Journal of Low Carbon Technologies, 2006, vol. 1, no. 3, pp. 201–216en_US
dc.identifier.issn17481317-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/17956-
dc.description.abstractArtificial neural networks (ANNs) are nowadays accepted as an alternative technology offering a way to tackle complex and ill-defined problems. They are not programmed in the traditional way but they are trained using past history data representing the behaviour of a system. They have been used in a number of diverse applications. Results presented in this paper are testimony to the potential of artificial neural networks as a design tool in many areas of building services engineering.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Low Carbon Technologiesen_US
dc.rights© Manchester University Press 2006en_US
dc.subjectArtificial neural networksen_US
dc.subjectEnergy predictionen_US
dc.subjectBuilding applicationsen_US
dc.titleArtificial neural networks in energy applications in buildingsen_US
dc.typeArticleen_US
dc.collaborationHigher Technical Institute Cyprusen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1093/ijlct/1.3.201en_US
dc.relation.issue3en_US
dc.relation.volume1en_US
cut.common.academicyear2005-2006en_US
dc.identifier.spage201en_US
dc.identifier.epage216en_US
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.fulltextWith Fulltext-
crisitem.journal.journalissn1748-1325-
crisitem.journal.publisherOxford University Press-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Άρθρα/Articles
Αρχεία σε αυτό το τεκμήριο:
Αρχείο Περιγραφή ΜέγεθοςΜορφότυπος
1-3-201.pdfFulltext224.47 kBAdobe PDFΔείτε/ Ανοίξτε
CORE Recommender
Δείξε τη σύντομη περιγραφή του τεκμηρίου

Page view(s)

289
Last Week
1
Last month
2
checked on 6 Νοε 2024

Download(s)

83
checked on 6 Νοε 2024

Google ScholarTM

Check

Altmetric


Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα