Articles | Volume 16, issue 5
https://doi.org/10.5194/acp-16-3399-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/acp-16-3399-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts
Thomas Schmidt
CORRESPONDING AUTHOR
Department of Energy and Semiconductor Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
John Kalisch
Department of Energy and Semiconductor Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
Elke Lorenz
Department of Energy and Semiconductor Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
Detlev Heinemann
Department of Energy and Semiconductor Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
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- Simulating clear-sky index increment correlations under mixed sky conditions using a fractal cloud model G. Lohmann et al. 10.1016/j.solener.2017.04.048
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- Hourly and sub-hourly ahead global horizontal solar irradiation forecasting via a novel deep learning approach: A case study Z. Bounoua & A. Mechaqrane 10.1016/j.susmat.2023.e00599
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Latest update: 16 Aug 2024
Short summary
We performed an irradiance forecast experiment based on analysis of
hemispheric sky images and evaluated results on a large data set of 99
pyranometers distributed over 10 × 12 km. We developed a surface
irradiance retrieval from cloud information derived from the images.
Very high resolution forecasts were processed up to 25 min. A main
finding is that forecast skill is enhanced in complex cloud conditions
leading to high variability in surface irradiance.
We performed an irradiance forecast experiment based on analysis of
hemispheric sky images and...
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