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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.authorBojic, Milorad-
dc.contributor.otherΚαλογήρου, Σωτήρης Α.-
dc.date.accessioned2009-05-28T11:29:03Zen
dc.date.accessioned2013-05-17T05:23:07Z-
dc.date.accessioned2015-12-02T10:20:36Z-
dc.date.available2009-05-28T11:29:03Zen
dc.date.available2013-05-17T05:23:07Z-
dc.date.available2015-12-02T10:20:36Z-
dc.date.issued2000-05-
dc.identifier.citationEnergy, 2000, vol. 25, no. 5, pp. 479-491en_US
dc.identifier.issn03605442-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1299-
dc.description.abstractArtificial neural networks (ANNs) have been used for the prediction of the energy consumption of a passive solar building. The building structure consists of one room with an inclined roof. Two cases were investigated, an all insulated building and a building with one wall made completely of masonry and the other walls made partially of masonry and thermal insulation. The investigation was performed for two seasons: winter, for which the building with the masonry-only wall is facing south, and summer, for which the building with the masonry-only wall is facing north. The building's thermal behaviour was evaluated by using a dynamic thermal building model constructed on the basis of finite volumes and time marching. The energy consumption of the building depends on whether all walls have insulation, on the thickness of the masonry and insulation and on the season. Simulated data for a number of cases were used to train an artificial neural network (ANN) in order to generate a mapping between the above easily measurable inputs and the desired output, i.e., the building energy consumption in kWh. The simulated buildings had walls varying from 15 cm to 60 cm in thickness. The objective of this work is to produce another simulation program, using ANNs, to model the thermal behaviour of the building. A multilayer recurrent architecture using the standard back-propagation learning algorithm has been applied. The results obtained for the training set are such that they yield a coefficient of multiple determination (R2 value) equal to 0.9985. The network was used subsequently for predictions of the energy consumption for cases other than the ones used for training. The coefficient of multiple determination obtained in this case was equal to 0.9991, which is very satisfactory. The ANN model proved to be much faster than the dynamic simulation programs.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofEnergyen_US
dc.rights© Elsevieren_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectEnergy consumptionen_US
dc.titleArtificial neural networks for the prediction of the energy consumption of a passive solar buildingen_US
dc.typeArticleen_US
dc.collaborationHigher Technical Institute Cyprusen_US
dc.collaborationUniversity of Kragujevacen_US
dc.subject.categoryMechanical Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.countrySerbiaen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/S0360-5442(99)00086-9en_US
dc.dept.handle123456789/54en
dc.relation.issue5en_US
dc.relation.volume25en_US
cut.common.academicyear2000-2001en_US
dc.identifier.spage479en_US
dc.identifier.epage491en_US
item.grantfulltextnone-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
crisitem.journal.journalissn0360-5442-
crisitem.journal.publisherElsevier-
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-
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