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Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/634A7774-91C8-4762-A955-1E18A7D585D8 | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.26897 | - |
| Language | en | - |
| Extent | 3,08 megabytes | en |
| Title | Long-term prediction of temperature data for the assessment of future trends of the building heating and cooling loads | en |
| Creator | Georgatou Christina | en |
| Creator | Γεωργατου Χριστινα | el |
| Contributor [Thesis Supervisor] | Kolokotsa Dionysia | en |
| Contributor [Thesis Supervisor] | Κολοκοτσα Διονυσια | el |
| Contributor [Committee Member] | Nikolaidis Nikolaos | en |
| Contributor [Committee Member] | Νικολαιδης Νικολαος | el |
| Contributor [Committee Member] | Kalaitzakis Kostas | en |
| Contributor [Committee Member] | Καλαϊτζακης Κωστας | el |
| Publisher | Technical University of Crete | en |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Academic Unit | Technical University of Crete::School of Environmental Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Μηχανικών Περιβάλλοντος | el |
| Content Summary | The 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 Item | Μεταπτυχιακή Διατριβή | el |
| Type of Item | Master Thesis | en |
| License | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en |
| Date of Item | 2015-07-03 | - |
| Date of Publication | 2015 | - |
| Subject | Consumption of energy | en |
| Subject | Energy efficiency | en |
| Subject | Fuel consumption | en |
| Subject | Fuel efficiency | en |
| Subject | energy consumption | en |
| Subject | consumption of energy | en |
| Subject | energy efficiency | en |
| Subject | fuel consumption | en |
| Subject | fuel efficiency | en |
| Subject | BIM (Building information modeling) | en |
| Subject | building information modeling | en |
| Subject | bim building information modeling | en |
| Subject | Artificial neural networks | en |
| Subject | Nets, Neural (Computer science) | en |
| Subject | Networks, Neural (Computer science) | en |
| Subject | Neural nets (Computer science) | en |
| Subject | neural networks computer science | en |
| Subject | artificial neural networks | en |
| Subject | nets neural computer science | en |
| Subject | networks neural computer science | en |
| Subject | neural nets computer science | en |
| Subject | Arma models | en |
| Subject | Buildings--Heating and ventilation | en |
| Subject | heating | en |
| Subject | buildings heating and ventilation | en |
| Bibliographic Citation | Χριστίνα 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, 2015 | el |
| Bibliographic Citation | Christina 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, 2015 | en |
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