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@INPROCEEDINGS{Albanese:1048849,
author = {Albanese, Mario and Carta, Daniele and Löhnert, Ulrich and
Benigni, Andrea},
title = {{A}ssessing the {I}mpact of {G}round-{B}ased {C}loud
{O}bservations on {P}hotovoltaic {G}eneration {F}orecast},
publisher = {IEEE},
reportid = {FZJ-2025-04954},
pages = {1-6},
year = {2025},
abstract = {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.},
month = {Oct},
date = {2025-10-14},
organization = {IECON 2025 – 51st Annual Conference
of the IEEE Industrial Electronics
Society, Madrid (Spain), 14 Oct 2025 -
17 Oct 2025},
cin = {ICE-1},
cid = {I:(DE-Juel1)ICE-1-20170217},
pnm = {1122 - Design, Operation and Digitalization of the Future
Energy Grids (POF4-112) / 1123 - Smart Areas and Research
Platforms (POF4-112)},
pid = {G:(DE-HGF)POF4-1122 / G:(DE-HGF)POF4-1123},
typ = {PUB:(DE-HGF)8},
doi = {10.1109/IECON58223.2025.11221083},
url = {https://juser.fz-juelich.de/record/1048849},
}