| Title: | Long-term temperature prediction with hybrid autoencoder algorithms |
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| Authors: | ID Pérez-Aracil, Jorge (Author)
ID Fister, Dušan (Author)
ID Marina, C. M. (Author)
ID Peláez-Rodriguez, César (Author)
ID Cornejo-Bueno, L. (Author)
ID Gutiérrez, P. A. (Author)
ID Giuliani, Matteo (Author)
ID Castelleti, A. (Author)
ID Salcedo-Sanz, Sancho (Author) |
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| Files: |  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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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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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| Keywords: | autoencoder, temperature prediction, hybrid models, heatwave |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 29.03.2024 |
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| Article acceptance date: | 01.08.2024 |
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| Publication date: | 08.08.2024 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2024 |
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| Number of pages: | 13 str. |
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| Numbering: | Vol. 23, [article no.] 100185 |
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| PID: | 20.500.12556/DKUM-91705  |
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| UDC: | 004.8 |
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| ISSN on article: | 1873-6793 |
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| COBISS.SI-ID: | 204807683 |
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| DOI: | 10.1016/j.acags.2024.100185 |
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| Copyright: | © 2024 The Author(s) |
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| Publication date in DKUM: | 29.01.2025 |
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| Views: | 177 |
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| Downloads: | 6 |
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| Metadata: |    |
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| Categories: | Misc.
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