Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/13369
DC FieldValueLanguage
dc.contributor.authorGeorgiou, Georgios C.-
dc.contributor.authorChristodoulides, Paul-
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2019-02-22T12:48:52Z-
dc.date.available2019-02-22T12:48:52Z-
dc.date.issued2018-06-
dc.identifier.citation5th IEEE International Energy Conference, 2018, 3-7 June, Limassol, Cyprusen_US
dc.identifier.isbn978-1-5386-3669-5-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/13369-
dc.description.abstractArtificial Neural Networks (ANNs) constitute a research area of high interest, for both practitioners and academics, as they are found very useful for solving complex problems that are difficult to solve using known and well developed conventional methods or techniques. They can be used for prediction, control, estimation, data clustering and many other applications that are found in everyday scenarios. This paper explains in brief the basic theory of ANNs, followed by a review of different studies related to ANNs used for applications in buildings such as energy management, systems control and energy prediction. It has been found that applying ANNs in buildings the energy consumption can be reduced, depending on the application. Furthermore, efficient control mechanisms also become possible, leading to the reduction of the energy consumption. Through this review, the reader will be able to recognise the value of ANNs and their big potential in buildings and energy sector, in general. Finally, an ANN-based structure for predicting the local RES generation and the load demand for a building is proposed.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2018 IEEE.en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectBuildingsen_US
dc.subjectEnergyen_US
dc.subjectRenewable Energyen_US
dc.titleImplementing artificial neural networks in energy building applications - A reviewen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conference5th IEEE International Energy Conferenceen_US
dc.identifier.doi10.1109/ENERGYCON.2018.8398847en_US
cut.common.academicyear2017-2018en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-2229-8798-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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