A point and interval forecasting of solar irradiance using different decomposition based hybrid models
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01020-9.pdf
Reference32 articles.
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2. Acikgoz H (2022) A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Appl Energy 305:117912.https://doi.org/10.1016/J.APENERGY.2021.117912
3. Ali M, Prasad R, Xiang Y et al (2021) Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology. Energy Rep 7:6700–6717. https://doi.org/10.1016/J.EGYR.2021.09.113
4. Benali L, Notton G, Fouilloy A, Voyant C, Dizene R (2019) Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. Renew Energy 132:871–884. https://doi.org/10.1016/j.renene.2018.08.044
5. Bouzgou H, Gueymard CA (2019) Fast short-term global solar irradiance forecasting with wrapper mutual information. Renew Energy 133:1055–1065. https://doi.org/10.1016/j.renene.2018.10.096
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