Short-term wind power forecasting using the hybrid model of multivariate variational mode decomposition (MVMD) and long short-term memory (LSTM) neural networks
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00202-024-02685-1.pdf
Reference74 articles.
1. Ahmed SD, Al-Ismail FS, Shafiullah M, Al-Sulaiman FA, El-Amin IM (2020) Grid integration challenges of wind energy: a review. IEEE Access 8:10857–10878
2. Avar A, Sheikh-El-Eslami MK (2021) Optimal DG placement in power markets from DG Owners’ perspective considering the impact of transmission costs. Electric Power Syst Res 196:107218
3. Avar MK (2022) Sheikh-El-Eslami, A new benefit-based transmission cost allocation scheme based on capacity usage differentiation. Electr Power Syst Res 208:107880
4. Tian Z, Li H, Li F (2021) A combination forecasting model of wind speed based on decomposition. Energy Rep 7:1217–1233
5. Qiao B, Liu J, Wu P, Teng Y (2022) Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps. Appl Soft Comput 129:109586
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