Improving model chain approaches for probabilistic solar energy forecasting through post-processing and machine learning

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SEEK ID: https://publications.h-its.org/publications/1900

SEEK ID: https://publications.h-its.org/publications/1900

DOI: 10.1007/s00376-024-4219-2

Research Groups: Computational Statistics

Publication type: Journal

Journal: Advances in Atmospheric Sciences

Publisher: Springer Science and Business Media LLC

Citation: Advances in Atmospheric Sciences, 42(2):297–312

Date Published: 28th Dec 2024

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Registered Mode: manually

Authors: Nina Horat, Sina Klerings, Sebastian Lerch

Citation
Horat, N., Klerings, S., & Lerch, S. (2024). Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning. In Advances in Atmospheric Sciences (Vol. 42, Issue 2, pp. 297–312). Springer Science and Business Media LLC. https://doi.org/10.1007/s00376-024-4219-2
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