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A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting. (2024). Zhang, Guowei ; Liu, DA ; Yang, DI ; Cheng, Runkun.
In: Energy.
RePEc:eee:energy:v:308:y:2024:i:c:s0360544224026690.

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  1. Research on Physically Constrained VMD-CNN-BiLSTM Wind Power Prediction. (2025). Zhou, XU ; Zhang, Yonggang ; Long, Yuwei ; Gu, YI ; Liu, Yongkang.
    In: Sustainability.
    RePEc:gam:jsusta:v:17:y:2025:i:3:p:1058-:d:1578678.

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  2. Wind power prediction based on improved self-attention mechanism combined with Bi-directional Temporal Convolutional Network. (2025). Lai, Ching-Ming ; Teh, Jiashen ; Shi, Jian.
    In: Energy.
    RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013088.

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References

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  1. Bai S, Kolter JZ, Koltun V. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling 2018. arXiv preprint arXiv: 180301271 2018.
    Paper not yet in RePEc: Add citation now
  2. Barjasteh, A. ; Ghafouri, S.H. ; Hashemi, M. A hybrid model based on discrete wavelet transform (DWT) and bidirectional recurrent neural networks for wind speed prediction. 2024 Eng Appl Artif Intell. 127 -
    Paper not yet in RePEc: Add citation now
  3. Chen, F. ; Yan, J. ; Liu, Y. ; Yan, Y. ; Tjernberg, L.B. A novel meta-learning approach for few-shot short-term wind power forecasting. 2024 Appl Energy. 362 -

  4. da Silva, R.G. ; Ribeiro, M.H.D.M. ; Moreno, S.R. ; Mariani, V.C. ; Coelho, L. ; dos, S. A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting. 2021 Energy. 216 -

  5. Emeksiz, C. ; Tan, M. Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach. 2022 Energy. 238 -

  6. Golyandina, N. ; Nekrutkin, V. ; Zhigljavsky, A.A. Analysis of time series structure: SSA and related techniques. 2001 CRC Press:
    Paper not yet in RePEc: Add citation now
  7. Hanifi, S. ; Zare-Behtash, H. ; Cammarano, A. ; Lotfian, S. Offshore wind power forecasting based on WPD and optimised deep learning methods. 2023 Renew Energy. 218 -

  8. Hou, G. ; Wang, J. ; Fan, Y. Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction. 2024 Energy. 286 -

  9. Jiang, L. ; Wang, Y. A wind power forecasting model based on data decomposition and cross-attention mechanism with cosine similarity. 2024 Elec Power Syst Res. 229 -
    Paper not yet in RePEc: Add citation now
  10. Jiang, Z. ; Che, J. ; He, M. ; Yuan, F. A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition. 2023 Renew Energy. 203 802-827

  11. Jiang, Z. ; Che, J. ; Wang, L. Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation. 2021 Energy Convers Manag. 250 -
    Paper not yet in RePEc: Add citation now
  12. Jonkers, J. ; Avendano, D.N. ; Van Wallendael, G. ; Van Hoecke, S. A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests. 2024 Appl Energy. 361 -

  13. Karijadi, I. ; Chou, S.-Y. ; Dewabharata, A. Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method. 2023 Renew Energy. 218 -

  14. Li, N. ; Dong, J. ; Liu, L. ; Li, H. ; Yan, J. A novel EMD and causal convolutional network integrated with Transformer for ultra short-term wind power forecasting. 2023 Int J Electr Power Energy Syst. 154 -
    Paper not yet in RePEc: Add citation now
  15. Li, Y. ; Sun, K. ; Yao, Q. ; Wang, L. A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm. 2024 Energy. 286 -

  16. Liu, H. ; Duan, Z. Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation. 2020 Energy Convers Manag. 203 -
    Paper not yet in RePEc: Add citation now
  17. Liu, H. ; Han, H. ; Sun, Y. ; Shi, G. ; Su, M. ; Liu, Z. Short-term wind power interval prediction method using VMD-RFG and Att-GRU. 2022 Energy. 251 -

  18. Liu, Z.-H. ; Wang, C.-T. ; Wei, H.-L. ; Zeng, B. ; Li, M. ; Song, X.-P. A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data. 2024 Expert Syst Appl. 247 -
    Paper not yet in RePEc: Add citation now
  19. Ma, Y. ; Yu, L. ; Zhang, G. Short-term wind power forecasting with an intermittency-trait-driven methodology. 2022 Renew Energy. 198 872-883

  20. Malhan, P. ; Mittal, M. A novel ensemble model for long-term forecasting of wind and hydro power generation. 2022 Energy Convers Manag. 251 -
    Paper not yet in RePEc: Add citation now
  21. Memarzadeh, G. ; Keynia, F. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. 2020 Energy Convers Manag. 213 -
    Paper not yet in RePEc: Add citation now
  22. Mi, X. ; Liu, H. ; Li, Y. Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. 2019 Energy Convers Manag. 180 196-205
    Paper not yet in RePEc: Add citation now
  23. Montero-Manso, P. ; Hyndman, R.J. Principles and algorithms for forecasting groups of time series: locality and globality. 2021 Int J Forecast. 37 1632-1653

  24. Moreno, S.R. ; Seman, L.O. ; Stefenon, S.F. ; Coelho, L. ; dos, S. ; Mariani, V.C. Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition. 2024 Energy. 292 -

  25. Peng, Z. ; Peng, S. ; Fu, L. ; Lu, B. ; Tang, J. ; Wang, K. A novel deep learning ensemble model with data denoising for short-term wind speed forecasting. 2020 Energy Convers Manag. 207 -
    Paper not yet in RePEc: Add citation now
  26. Qian, Z. ; Pei, Y. ; Zareipour, H. ; Chen, N. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. 2019 Appl Energy. 235 939-953

  27. Qu, Z. ; Hou, X. ; Li, J. ; Hu, W. Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation. 2024 Energy. 290 -

  28. Shang, Z. ; Chen, Y. ; Chen, Y. ; Guo, Z. ; Yang, Y. Decomposition-based wind speed forecasting model using causal convolutional network and attention mechanism. 2023 Expert Syst Appl. 223 -
    Paper not yet in RePEc: Add citation now
  29. Shao, Z. ; Han, J. ; Zhao, W. ; Zhou, K. ; Yang, S. Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field. 2022 Energy Convers Manag. 269 -
    Paper not yet in RePEc: Add citation now
  30. Sun, S. ; Du, Z. ; Jin, K. ; Li, H. ; Wang, S. Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy. 2023 Appl Energy. 350 -

  31. Sun, W. ; Tan, B. ; Wang, Q. Multi-step wind speed forecasting based on secondary decomposition algorithm and optimized back propagation neural network. 2021 Appl Soft Comput. 113 -
    Paper not yet in RePEc: Add citation now
  32. Sun, Z. ; mingyu, Zhao ; yan, Dong ; Cao, xin ; Sun, H. Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales. 2021 Energy. 221 -

  33. Tawn, R. ; Browell, J. A review of very short-term wind and solar power forecasting. 2022 Renew Sustain Energy Rev. 153 -

  34. Torres, M.E. ; Colominas, M.A. ; Schlotthauer, G. ; Flandrin, P. A complete ensemble empirical mode decomposition with adaptive noise. 2011 :
    Paper not yet in RePEc: Add citation now
  35. Wang, C. ; Lin, H. ; Hu, H. ; Yang, M. ; Ma, L. A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction. 2024 Energy. 293 -

  36. Wang, Q. ; Pan, L. ; Wang, H. ; Wang, X. ; Zhu, Y. Short-term wind power probabilistic forecasting using a new neural computing approach: GMC-DeepNN-PF. 2022 Appl Soft Comput. 126 -
    Paper not yet in RePEc: Add citation now
  37. Wang, S. ; Wang, J. ; Lu, H. ; Zhao, W. A novel combined model for wind speed prediction – combination of linear model, shallow neural networks, and deep learning approaches. 2021 Energy. 234 -
    Paper not yet in RePEc: Add citation now
  38. Wang, Y. ; Zou, R. ; Liu, F. ; Zhang, L. ; Liu, Q. A review of wind speed and wind power forecasting with deep neural networks. 2021 Appl Energy. 304 -

  39. Wu, B. ; Wang, L. Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting. 2024 Energy. 288 -

  40. Wu, Z. ; Tan, Z.-M. ; Pietrafesa, L. Spectral analysis of a time series: from an additive perspective to a multiplicative perspective. 2023 Appl Comput Harmon Anal. 63 94-112
    Paper not yet in RePEc: Add citation now
  41. Wu, Z. ; Xia, X. ; Xiao, L. ; Liu, Y. Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting. 2020 Appl Energy. 261 -

  42. Xiang, L. ; Li, J. ; Hu, A. ; Zhang, Y. Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method. 2020 Energy Convers Manag. 220 -
    Paper not yet in RePEc: Add citation now
  43. Yang, Q. ; Deng, C. ; Chang, X. Ultra-short-term/short-term wind speed prediction based on improved singular spectrum analysis. 2022 Renew Energy. 184 36-44

  44. Yang, T. ; Yang, Z. ; Li, F. ; Wang, H. A short-term wind power forecasting method based on multivariate signal decomposition and variable selection. 2024 Appl Energy. 360 -

  45. Yin, H. ; Dong, Z. ; Chen, Y. ; Ge, J. ; Lai, L.L. ; Vaccaro, A. An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization. 2017 Energy Convers Manag. 150 108-121
    Paper not yet in RePEc: Add citation now
  46. Yin, H. ; Ou, Z. ; Huang, S. ; Meng, A. A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition. 2019 Energy. 189 -

  47. Yu, L. ; Ma, Y. ; Ma, Y. ; Zhang, G. A complexity-trait-driven rolling decomposition-reconstruction-ensemble model for short-term wind power forecasting. 2022 Sustain Energy Technol Assessments. 49 -
    Paper not yet in RePEc: Add citation now
  48. Yu, M. ; Niu, D. ; Gao, T. ; Wang, K. ; Sun, L. ; Li, M. A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism. 2023 Energy. 269 -

  49. Zhang, C. ; Ma, H. ; Hua, L. ; Sun, W. ; Nazir, M.S. ; Peng, T. An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction. 2022 Energy. 254 -

  50. Zhang, D. ; Chen, B. ; Zhu, H. ; Goh, H.H. ; Dong, Y. ; Wu, T. Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model. 2023 Energy. 285 -

  51. Zhang, G. ; Liu, D. Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting. 2020 Energy Convers Manag. 226 -
    Paper not yet in RePEc: Add citation now
  52. Zhang, G. ; Zhang, Y. ; Wang, H. ; Liu, D. ; Cheng, R. ; Yang, D. Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network. 2024 Energy. 288 -

  53. Zhang, S. ; Chen, Y. ; Xiao, J. ; Zhang, W. ; Feng, R. Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism. 2021 Renew Energy. 174 688-704

  54. Zhang, S. ; Liu, M. ; Liu, M. ; Lei, Z. ; Zeng, G. ; Chen, Z. Day-ahead wind power prediction using an ensemble model considering multiple indicators combined with error correction. 2023 Appl Soft Comput. 148 -
    Paper not yet in RePEc: Add citation now
  55. Zhang, W. ; Qu, Z. ; Zhang, K. ; Mao, W. ; Ma, Y. ; Fan, X. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. 2017 Energy Convers Manag. 136 439-451
    Paper not yet in RePEc: Add citation now
  56. Zhang, Y. ; Chen, B. ; Pan, G. ; Zhao, Y. A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. 2019 Energy Convers Manag. 195 180-197
    Paper not yet in RePEc: Add citation now
  57. Zhang, Y. ; Wang, H. ; Wang, J. ; Cheng, X. ; Wang, T. ; Zhao, Z. Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system. 2024 Energy. 292 -

  58. Zhang, Z. ; Wang, J. ; Wei, D. ; Luo, T. ; Xia, Y. A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network. 2023 Renew Energy. 204 11-23

  59. Zhao, Y. ; Pan, S. ; Zhao, Y. ; Liao, H. ; Ye, L. ; Zheng, Y. Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration. 2024 Energy. 288 -

  60. Zheng, H. ; Hu, Z. ; Wang, X. ; Ni, J. ; Cui, M. VMD-CAT: a hybrid model for short-term wind power prediction. 2023 Energy Rep. 9 199-211
    Paper not yet in RePEc: Add citation now
  61. Zhu, J. ; He, Y. ; Yang, X. ; Yang, S. Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network. 2024 Energy Convers Manag. 301 -
    Paper not yet in RePEc: Add citation now

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