Forecasting solar power production by using satellite images

Stefanov, Dimitar (avtor), Demšar, Jure (avtor)

Increased integration of photo-voltaic capacities across the world has caused the electrical grid to become more difficult to maintain. In order to operate the grid in an efficient manner, its operators need to rely on accurate forecasts of the solar power generation. For this purpose, our work focused on predicting 2-h ahead solar power generation at 15 min intervals – a typical resolution requirement for solar power plants and system operation measurements. We made solar power generation forecasts for 233 different locations across Slovenia – more extensive research regarding the number of photo-voltaic locations than what can be found in the literature on this topic. We showed that the state-of-the-art deep learning architecture called Temporal Fusion Transformer outperforms well-established benchmarks in solar forecasting by significant margins across all metrics and training settings considered. Our investigation of feature importance also proved that the Temporal Fusion Transformer is capable of extracting a sufficient amount of information from satellite images meaning that such images can be used as a decent replacement for solar power data when the latter is not available.

• Jezik: Angleški jezik
• Ključne besede: solar forecasting, time series forecasting, deep learning, satellite images
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 13 str.
• Številčenje: Vol. 300, Art.113823
• PID: 20.500.12556/RUL-175512
• UDK: 004.85:004.93:621.311.243
• ISSN pri članku: 0038-092X
• DOI: 10.1016/j.solener.2025.113823
• COBISS.SI-ID: 245205763
• Datum objave v RUL: 30.10.2025

Gradivo je del revije

• Naslov: Solar energy
• Skrajšan naslov: Sol. energy
• Založnik: Association for Applied Solar Energy, Elsevier
• ISSN: 0038-092X
• COBISS.SI-ID: 5228039

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: napovedovanje proizvodnje sončne energije, napovedovanje časovnih vrst, globoko učenje, satelitske slike

Projekti

• Financer: NSF - National Science Foundation
• Program financ.: Directorate for Geosciences
• Številka projekta: 8200684
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