LSTM–GAN based cloud movement prediction in satellite images for PV forecast
Author:
Funder
National Research Foundation of Korea
Korea Electric Power Corporation
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-022-04333-7.pdf
Reference26 articles.
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2. Catalina A, Alaíz CM, Dorronsoro JR (2020) Combining numerical weather predictions and satellite data for PV energy nowcasting. IEEE Trans Sustain Energy 11(3):1930–1937. https://doi.org/10.1109/TSTE.2019.2946621
3. Cheng L, Zang H, Wei Z et al (2022) Short-term solar power prediction learning directly from satellite images with regions of interest. IEEE Trans Sustain Energy 13(1):629–639. https://doi.org/10.1109/TSTE.2021.3123476
4. Colak I, Yesilbudak M, Genc N, et al (2015) Multi-period prediction of solar radiation using ARMA and ARIMA models. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp 1045–1049, https://doi.org/10.1109/ICMLA.2015.33
5. Du P (2019) Ensemble machine learning-based wind forecasting to combine NWP output with data from weather station. IEEE Trans Sustain Energy 10(4):2133–2141. https://doi.org/10.1109/TSTE.2018.2880615
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