Open Access
| Issue |
E3S Web Conf.
Volume 466, 2023
2023 8th International Conference on Advances in Energy and Environment Research & Clean Energy and Energy Storage Technology Forum (ICAEER & CEEST 2023)
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|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 5 | |
| Section | Energy Material Research and Power Generation System Analysis | |
| DOI | https://doi.org/10.1051/e3sconf/202346601011 | |
| Published online | 15 December 2023 | |
- H Liu, X Mi, Y Li. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM [J]. Energy Conversion and Management, 2018, 159: 54-64. [CrossRef] [Google Scholar]
- Z Ma, H Chen, J Wang, et al. Application of hybrid model based on double decomposition,Error correction and deep learning in short-term wind speed prediction[J]. Energy Conversion and Management, 2020, 205. [Google Scholar]
- Z Wu, N E Huang. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1(1), 1-41. [CrossRef] [Google Scholar]
- Shahram H,Hossein Z,Andrea C, et al. Offshore wind power forecasting based on WPD and optimised deep learning methods[J]. Renewable Energy, 2023,218: 119241. [CrossRef] [Google Scholar]
- Shahid F, Wood A D, Nisar H, et al. Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning[J]. Renewable and Sustainable Energy Reviews,2022,167: 112700. [CrossRef] [Google Scholar]
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