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001048849 041__ $$aEnglish
001048849 1001_ $$0P:(DE-Juel1)199770$$aAlbanese, Mario$$b0$$eCorresponding author$$ufzj
001048849 1112_ $$aIECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society$$cMadrid$$d2025-10-14 - 2025-10-17$$gIECON 2025$$wSpain
001048849 245__ $$aAssessing the Impact of Ground-Based Cloud Observations on Photovoltaic Generation Forecast
001048849 260__ $$bIEEE$$c2025
001048849 300__ $$a1-6
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001048849 520__ $$aThe widespread integration of photovoltaic (PV) systems into modern power grids poses several operational challenges, primarily due to the stochastic nature of weather conditions, which leads to uncertainty in power generation. In this context, accurate forecasting of PV output becomes critical, especially for electricity markets and grid management. This paper investigates the relationship between non-conventional weather variables (i.e., ground-based cloud observations) and PV power generation, and evaluates the impact of incorporating such data into a machine learning model to improve forecasting performance. The analysis is conducted using real power output and ground-based meteorological data collected at Forschungszentrum Jülich, in Germany.
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001048849 536__ $$0G:(DE-HGF)POF4-1123$$a1123 - Smart Areas and Research Platforms (POF4-112)$$cPOF4-112$$fPOF IV$$x1
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001048849 7001_ $$0P:(DE-Juel1)186779$$aCarta, Daniele$$b1$$ufzj
001048849 7001_ $$0P:(DE-Juel1)129225$$aLöhnert, Ulrich$$b2$$ufzj
001048849 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b3$$ufzj
001048849 773__ $$a10.1109/IECON58223.2025.11221083
001048849 8564_ $$uhttps://ieeexplore.ieee.org/document/11221083
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001048849 9141_ $$y2025
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001048849 9201_ $$0I:(DE-Juel1)ICE-1-20170217$$kICE-1$$lModellierung von Energiesystemen$$x0
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