Hindcasting with Cluster-Based Analogues

Author:

Balsa CarlosORCID,Rodrigues Carlos V.ORCID,Araújo Leonardo,Rufino JoséORCID

Publisher

Springer International Publishing

Reference17 articles.

1. National Data Buoy Center. https://www.nature.com/nature/. Accessed 20 Oct 2020

2. Alessandrini, S., Monache, L.D., Sperati, S., Cervone, G.: An analog ensemble for short-term probabilistic solar power forecast. Appl. Energy 157, 95–110 (2015). https://doi.org/10.1016/j.apenergy.2015.08.011

3. Alessandrini, S., Monache, L.D., Sperati, S., Nissen, J.N.: A novel application of an analog ensemble for short-term wind power forecasting. Renew. Energy 76, 768–781 (2015). https://doi.org/10.1016/j.renene.2014.11.061

4. Advances in Intelligent Systems and Computing;L Araújo,2021

5. Balsa, C., Rodrigues, C.V., Lopes, I., Rufino, J.: Using analog ensembles with alternative metrics for hindcasting with multistations. ParadigmPlus 1(2), 1–17 (2020). https://journals.itiud.org/index.php/paradigmplus/article/view/11

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1. Reconstruction of Meteorological Records with PCA-Based Analog Ensemble Methods;Lecture Notes in Networks and Systems;2024

2. PCAnEn - Hindcasting with Analogue Ensembles of Principal Components;CSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI);2023

3. Cluster-Based Analogue Ensembles for Hindcasting with Multistations;Computation;2022-06-02

4. An Exploratory Study on Hindcasting with Analogue Ensembles of Principal Components;Communications in Computer and Information Science;2022

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