%0 Conference Paper
%A Albanese, Mario
%A Carta, Daniele
%A Löhnert, Ulrich
%A Benigni, Andrea
%T Assessing the Impact of Ground-Based Cloud Observations on Photovoltaic Generation Forecast
%I IEEE
%M FZJ-2025-04954
%P 1-6
%D 2025
%X The 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.
%B IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society
%C 14 Oct 2025 - 17 Oct 2025, Madrid (Spain)
Y2 14 Oct 2025 - 17 Oct 2025
M2 Madrid, Spain
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%R 10.1109/IECON58223.2025.11221083
%U https://juser.fz-juelich.de/record/1048849