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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/5CD6B075-3FA8-42DE-80E8-0FBB6DF231E1 | - |
| Αναγνωριστικό | https://www.sciencedirect.com/science/article/pii/S0378778815300839?via%3Dihub | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.enbuild.2015.06.054 | - |
| Γλώσσα | en | - |
| Μέγεθος | 8 pages | en |
| Τίτλος | Prediction of outdoor air temperature using neural networks: application in 4 european cities | en |
| Δημιουργός | Papantoniou Sotirios | en |
| Δημιουργός | Παπαντωνιου Σωτηριος | el |
| Δημιουργός | Kolokotsa Dionysia | en |
| Δημιουργός | Κολοκοτσα Διονυσια | el |
| Εκδότης | Elsevier | en |
| Περίληψη | The aim of this paper is to present the development and evaluation of neural network based identification algorithms for the prediction of outdoor air temperature using acquired data from four European cities (Ancona - Italy, Chania - Greece, Granada - Spain and Mollet - Spain). Different neural network topologies (feed forward, cascade and elman) have been tested to identify the most suitable for each city. The efficiency of the prediction is validated by comparing predicted and measured outdoor air temperature. Furthermore, statistical tools such as R2, and root mean square error (rmse) are used to evaluate the annual performance of the neural network. The comparison of measured and predicted outdoor air temperature (R2 > 0.9, rmse <2 °C) confirms the accurate training of the neural network for all four European cities. All work has been contacted using Matlab's environment. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-19 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Neural networks | en |
| Θεματική Κατηγορία | Outdoor air temperature prediction | en |
| Βιβλιογραφική Αναφορά | S. Papantoniou and D.-D. Kolokotsa, "Prediction of outdoor air temperature using neural networks: application in 4 european cities," Energ. Buildings, vol. 114, pp. 72-79, Feb. 2016. doi: 10.1016/j.enbuild.2015.06.054 | en |
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