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https://hdl.handle.net/20.500.14279/4405
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Kalogirou, Soteris A. | - |
dc.date.accessioned | 2013-03-04T10:35:38Z | en |
dc.date.accessioned | 2013-05-17T10:30:17Z | - |
dc.date.accessioned | 2015-12-09T12:08:13Z | - |
dc.date.available | 2013-03-04T10:35:38Z | en |
dc.date.available | 2013-05-17T10:30:17Z | - |
dc.date.available | 2015-12-09T12:08:13Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Advances in Building Energy Research, 2009, vol. 3, no. 1, pp. 83-119 | en_US |
dc.identifier.issn | 17562201 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/4405 | - |
dc.description.abstract | The major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genetic algorithms (GAs) may play an important role in modelling and prediction of the performance of various energy systems in buildings. The chapter initially presents artificial neural networks and genetic algorithms and outlines an understanding of how they operate by way of presenting a number of problems in the different disciplines of energy applications in buildings including environmental parameters, renewable energy systems, naturally ventilated buildings, energy consumption and conservation, and HVAC systems. The various applications are presented in a thematic rather than a chronological or any other order. Results presented in this chapter are testimony of the potential of artificial neural networks and genetic algorithms as design tools in many areas of energy applications in buildings | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Advances in Building Energy Research | en_US |
dc.rights | © Taylor & Francis | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Genetic algorithms | en_US |
dc.subject | Environmental parameters | en_US |
dc.subject | Renewable energy systems | en_US |
dc.subject | Naturally ventilated buildings | en_US |
dc.subject | Energy consumption and conservation | en_US |
dc.subject | HVAC systems | en_US |
dc.title | Artificial neural networks and genetic algorithms in energy applications in buildings | en_US |
dc.type | Article | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Environmental Engineering | en_US |
dc.journals | Subscription | en_US |
dc.review | peer reviewed | - |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.3763/aber.2009.0304 | en_US |
dc.dept.handle | 123456789/141 | en |
dc.relation.issue | 1 | en_US |
dc.relation.volume | 3 | en_US |
cut.common.academicyear | 2009-2010 | en_US |
dc.identifier.spage | 83 | en_US |
dc.identifier.epage | 119 | en_US |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 1751-2549 | - |
crisitem.journal.publisher | Taylor & Francis | - |
crisitem.author.dept | Department of Mechanical Engineering and Materials Science and Engineering | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4497-0602 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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