TY  - CONF
AU  - Albanese, Mario
AU  - Carta, Daniele
AU  - Löhnert, Ulrich
AU  - Benigni, Andrea
TI  - Assessing the Impact of Ground-Based Cloud Observations on Photovoltaic Generation Forecast
PB  - IEEE
M1  - FZJ-2025-04954
SP  - 1-6
PY  - 2025
AB  - 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.
T2  - IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society
CY  - 14 Oct 2025 - 17 Oct 2025, Madrid (Spain)
Y2  - 14 Oct 2025 - 17 Oct 2025
M2  - Madrid, Spain
LB  - PUB:(DE-HGF)8
DO  - DOI:10.1109/IECON58223.2025.11221083
UR  - https://juser.fz-juelich.de/record/1048849
ER  -