| Naslov: | Long-term temperature prediction with hybrid autoencoder algorithms |
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| Avtorji: | ID Pérez-Aracil, Jorge (Avtor)
ID Fister, Dušan (Avtor)
ID Marina, C. M. (Avtor)
ID Peláez-Rodriguez, César (Avtor)
ID Cornejo-Bueno, L. (Avtor)
ID Gutiérrez, P. A. (Avtor)
ID Giuliani, Matteo (Avtor)
ID Castelleti, A. (Avtor)
ID Salcedo-Sanz, Sancho (Avtor) |
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| Datoteke: |  1-s2.0-S2590197424000326-main.pdf (1,82 MB)
MD5: 7DB04D9F78E0C33C9B8286C88A3C8EBF
 https://www.sciencedirect.com/science/article/pii/S2590197424000326?via%3Dihub
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
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| Opis: | This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as input of a neural network, which will provide the final prediction output. Both approaches are tested in long-term air temperature prediction in European cities: seven European locations where major heat waves occurred have been considered. The longterm temperature prediction for the entire year of the heatwave events has been analysed. Results show that the proposed approaches can obtain accurate long-term (up to 4 weeks) temperature prediction, improving Persistence and Climatology in the benchmark models compared. In heatwave periods, where the persistence of the temperature is extremely high, our approach beat the persistence operator in three locations and works similarly in the rest of the cases, showing the potential of this AE-based method for long-term temperature prediction. |
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| Ključne besede: | autoencoder, temperature prediction, hybrid models, heatwave |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 29.03.2024 |
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| Datum sprejetja članka: | 01.08.2024 |
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| Datum objave: | 08.08.2024 |
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| Založnik: | Elsevier |
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| Leto izida: | 2024 |
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| Št. strani: | 13 str. |
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| Številčenje: | Vol. 23, [article no.] 100185 |
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| PID: | 20.500.12556/DKUM-91705  |
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| UDK: | 004.8 |
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| COBISS.SI-ID: | 204807683 |
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| DOI: | 10.1016/j.acags.2024.100185 |
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| ISSN pri članku: | 1873-6793 |
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| Avtorske pravice: | © 2024 The Author(s) |
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| Datum objave v DKUM: | 29.01.2025 |
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| Število ogledov: | 178 |
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| Število prenosov: | 6 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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