Probabilistic end-to-end irradiance forecasting through pre-trained deep learning models using all-sky-images
Abstract. −2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Probabilistic end-to-end irradiance forecasting through pre-trained deep learning models using all-sky-images ; volume:20 ; year:2024 ; pages:129-158 ; extent:30
Advances in science and research ; 20 (2024), 129-158 (gesamt 30)
- Klassifikation
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Wirtschaft
- DOI
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10.5194/asr-20-129-2024
- URN
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urn:nbn:de:101:1-2024010403154986963678
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:25 MESZ
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