Open Access
| Issue |
E3S Web Conf.
Volume 603, 2025
International Symposium on Green and Sustainable Technology (ISGST 2024)
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 7 | |
| Section | Renewable Energy Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202560303003 | |
| Published online | 15 January 2025 | |
- S. Farah, W. David A, N. Humaira, Z. Aneela, and E. Steffen, “Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning,” Renew. Sustain. Energy Rev, vol. 167, p. 112700, Oct. 2022. http://doi.Org/10.1016/j.rser.2022.112700. [CrossRef] [Google Scholar]
- IEA, Renewables, IEA, Paris (2022) https://www.iea.org/reports/renewables-2022, Licence: CC BY 4.0 [Google Scholar]
- M. Abuella and B. Chowdhury, “Solar power forecasting using artificial neural networks,” in 2015 North American Power Symposium (NAPS), IEEE, Oct. 2015, pp. 1–5.http://doi.org/10.1109/NAPS.2015.7335176. [Google Scholar]
- N. Elizabeth Michael, M. Mishra, S. Hasan, and A. Al-Durra, “Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique,” Energies, vol. 15, no. 6, p. 2150, Mar. 2022.http://doi.org/10.3390/en15062150. [CrossRef] [Google Scholar]
- S. Jaidee and W. Pora, “Very Short-Term Solar Power Forecast using Data from NWP Model,” in 2019 4th International Conference on Information Technology (InCIT), IEEE, Oct. 2019, pp. 44–49. http://doi.org/10.1109/INaT.2019.8912012. [CrossRef] [Google Scholar]
- M. A. F. B. Lima, P. C. M. Carvalho, L. M. Fernandez-Ramirez, and A. P. S. Braga, “Improving solar forecasting using Deep Learning and Portfolio Theory integration,” Energy, vol. 195, p. 117016, Mar. 2020. http://doi.org/10.1016/j.energy.2020.117016 . [CrossRef] [Google Scholar]
- A. K. Mandal, R. Sen, S. Goswami, and B. Chakraborty, “Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance,” Symmetry, vol. 13, no. 8, p. 1544, Aug. 2021. http://doi.org/10.3390/sym13081544 . [CrossRef] [Google Scholar]
- J. Ospina, A. Newaz, and M. O. Faruque, “Forecasting of PV plant output using hybrid wavelet-based LSTM-DNN structure model,” IET Renewable Power Generation, vol. 13, no. 7, pp. 1087–1095, May 2019.http://doi.org/10.1049/iet-rpg.2018.5779. [CrossRef] [Google Scholar]
- C. Poolla and A. K. Ishihara, “Localized solar power prediction based on weather data from local history and global forecasts,” in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), IEEE, Jun. 2018, pp. 2341–2345. http://doi.org/10.1109/PVSC.2018.8548173. [CrossRef] [Google Scholar]
- Q. Qiang, S. Guoqiang, X. Wei, Y. Minghui, W. Zhinong, and Z. Haixiang, “Distribution System State Estimation Based on Pseudo Measurement Modeling Using Convolutional Neural Network,” in 2018 China International Conference on Electricity Distribution (CICED), IEEE, Sep. 2018, pp. 2416–2420. http://doi.org/10.1109/aCED.2018.8592565. [CrossRef] [Google Scholar]
- H. Zhou, Q. Liu, K. Yan, and Y. Du, “Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, Jan. 2021.http://doi.org/10.1155/2021/9249387. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.