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Long-term prediction of temperature data for the assessment of future trends of the building heating and cooling loads

Georgatou Christina

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URIhttp://purl.tuc.gr/dl/dias/634A7774-91C8-4762-A955-1E18A7D585D8-
Identifierhttps://doi.org/10.26233/heallink.tuc.26897-
Languageen-
Extent3,08 megabytesen
TitleLong-term prediction of temperature data for the assessment of future trends of the building heating and cooling loadsen
CreatorGeorgatou Christinaen
CreatorΓεωργατου Χριστιναel
Contributor [Thesis Supervisor]Kolokotsa Dionysiaen
Contributor [Thesis Supervisor]Κολοκοτσα Διονυσιαel
Contributor [Committee Member]Nikolaidis Nikolaosen
Contributor [Committee Member]Νικολαιδης Νικολαοςel
Contributor [Committee Member]Kalaitzakis Kostasen
Contributor [Committee Member]Καλαϊτζακης Κωσταςel
PublisherTechnical University of Creteen
PublisherΠολυτεχνείο Κρήτηςel
Academic UnitTechnical University of Crete::School of Environmental Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Μηχανικών Περιβάλλοντοςel
Content SummaryThe present work focuses on the long term prediction of temperature data employing neural network models. Primarily, a benchmarking auto regressive model is developed. Then, different neural networks are developed regarding the network type, the training function and the training intervals. Temperature predictions are calculated for ten and for five year intervals. Each model’s results are compared with the corresponding real temperature data, in terms of mean, maximum and minimum temperature values, cooling degree days and frequency distribution. The best predicted temperature data are used as outdoor temperature for the heating and cooling loads calculations of a typical office building. The building simulation model which is used for the energy demand calculations is the open source ESP-r model. The results indicate a relative accurate potential of the neural networks for the simulation of the mean temperature data and prediction of the cooling degree days. Regarding the high temperature values and the maximum peaks, the neural network models are unable to reach precise values, due to the lack of similar training data. As a result, the cooling loads calculated from neural network predictions are underestimated, while the heating loads prediction is more accurate.en
Type of ItemΜεταπτυχιακή Διατριβήel
Type of ItemMaster Thesisen
Licensehttp://creativecommons.org/licenses/by-nc-sa/4.0/en
Date of Item2015-07-03-
Date of Publication2015-
SubjectConsumption of energyen
SubjectEnergy efficiencyen
SubjectFuel consumptionen
SubjectFuel efficiencyen
Subjectenergy consumptionen
Subjectconsumption of energyen
Subjectenergy efficiencyen
Subjectfuel consumptionen
Subjectfuel efficiencyen
SubjectBIM (Building information modeling)en
Subjectbuilding information modelingen
Subjectbim building information modelingen
SubjectArtificial neural networksen
SubjectNets, Neural (Computer science)en
SubjectNetworks, Neural (Computer science)en
SubjectNeural nets (Computer science)en
Subjectneural networks computer scienceen
Subjectartificial neural networksen
Subjectnets neural computer scienceen
Subjectnetworks neural computer scienceen
Subjectneural nets computer scienceen
SubjectArma modelsen
SubjectBuildings--Heating and ventilationen
Subjectheatingen
Subjectbuildings heating and ventilationen
Bibliographic CitationΧριστίνα Γεωργάτου, "Long-term prediction of temperature data for the assessment of future trends of the building heating and cooling loads", Μεταπτυχιακή Διατριβή, Σχολή Μηχανικών Περιβάλλοντος, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2015el
Bibliographic CitationChristina Georgatou, "Long-term prediction of temperature data for the assessment of future trends of the building heating and cooling loads", Master Thesis, School of Environmental Engineering, Technical University of Crete, Chania, Greece, 2015en

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