Intrahour Direct Normal Irradiance Forecasting Based on Sky Image Processing and Time-Series Analysis
DOI:
https://doi.org/10.52825/solarpaces.v1i.648Keywords:
Direct Normal Irradiance, Sky-Imaging System, Hybrid ModelAbstract
The present paper exhibits a hybrid model for intrahour forecasting of direct normal irradiance (DNI). It combines a knowledge-based model, which is used for clear-sky DNI forecasting from DNI measurements, with a machine-learning-based model, that evaluates the impact of atmospheric disturbances on the solar resource, through the processing of high dynamic range sky images provided by a ground-based camera. The performance of the hybrid model is compared with that of two machine learning models based on past DNI observations only. The results highlight the pertinence of combining knowledge-based models with data-driven models, and of integrating sky-imaging data in the DNI forecasting process.
Parts of this paper were published as journal article
Karout, Y.; Thil, S.; Eynard, J.; Guillot, E.; Grieu, S. Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data. Solar Energy. 2023, 249, 541-558. https://doi.org/10.1016/j.solener.2022.11.032
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Copyright (c) 2024 Youssef Karout, Stéphane Thil, Julien Eynard, Emmanuel Guillot, Stéphane Grieu
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Horizon 2020
Grant numbers 823802
