Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/307709 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
HEMF Working Paper No. 07/2024
Verlag: 
University of Duisburg-Essen, House of Energy Markets & Finance (HEMF), Essen
Zusammenfassung: 
Accurate forecasting of solar PV generation is critical for integrating renewable energy into power systems. This paper presents a multivariate probabilistic forecasting model that addresses the challenges posed by imbalanced data resulting from day and night-time periods in solar photovoltaic (PV) generation. The proposed approach offers a robust and accurate method for predicting solar PV output by incorporating forecast updates and modeling the temporal interdependencies. The methodology is applied to a case study in France, demonstrating effectiveness across different spatial granularities and forecast horizons. The model uses advanced data handling methods combined with copula models, resulting in improved Energy Scores and Variogram-based Scores. These improvements underscore the importance of addressing imbalanced data and utilizing multivariate models with repeated updates to enhance solar forecasting accuracy. This work contributes to advancing forecasting techniques essential for integrating renewable energy into power grids, supporting the global transition to a sustainable energy future.
Schlagwörter: 
Multivariate probabilistic forecasts
Forecast updates
Solar generation
Copula
Dokumentart: 
Working Paper

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