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Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization

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  1. Wuyue An & Lin Wang & Yu‐Rong Zeng, 2023. "Text‐based soybean futures price forecasting: A two‐stage deep learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 312-330, March.
  2. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
  3. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
  4. Jiacheng Huang & Xiaowen Zhang & Xuchu Jiang, 2023. "Short-term power load forecasting based on the CEEMDAN-TCN-ESN model," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-26, October.
  5. Liu, Yulong & Jin, Tao & Mohamed, Mohamed A., 2023. "A novel dual-attention optimization model for points classification of power quality disturbances," Applied Energy, Elsevier, vol. 339(C).
  6. Amir Abdul Majid, 2022. "Forecasting Monthly Wind Energy Using an Alternative Machine Training Method with Curve Fitting and Temporal Error Extraction Algorithm," Energies, MDPI, vol. 15(22), pages 1-24, November.
  7. Zhao, Ning & Su, Yi & Dai, Xianxing & Jia, Shaomin & Wang, Xuewei, 2024. "A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction," Applied Energy, Elsevier, vol. 369(C).
  8. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "A hybrid VMD based contextual feature representation approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 219(P1).
  9. Zhang, Guowei & Zhang, Yi & Wang, Hui & Liu, Da & Cheng, Runkun & Yang, Di, 2024. "Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network," Energy, Elsevier, vol. 288(C).
  10. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
  11. Yu, Chuanjin & Fu, Suxiang & Wei, ZiWei & Zhang, Xiaochi & Li, Yongle, 2024. "Multi-feature-fused generative neural network with Gaussian mixture for multi-step probabilistic wind speed prediction," Applied Energy, Elsevier, vol. 359(C).
  12. Joseph, Lionel P. & Deo, Ravinesh C. & Casillas-Pérez, David & Prasad, Ramendra & Raj, Nawin & Salcedo-Sanz, Sancho, 2024. "Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model," Applied Energy, Elsevier, vol. 359(C).
  13. Yang, Dongchuan & Li, Mingzhu & Guo, Ju-e & Du, Pei, 2024. "An attention-based multi-input LSTM with sliding window-based two-stage decomposition for wind speed forecasting," Applied Energy, Elsevier, vol. 375(C).
  14. Wu, Zhou & Zeng, Shaoxiong & Jiang, Ruiqi & Zhang, Haoran & Yang, Zhile, 2023. "Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks," Energy, Elsevier, vol. 270(C).
  15. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
  16. Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
  17. Wu, Binrong & Wang, Lin, 2024. "Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting," Energy, Elsevier, vol. 288(C).
  18. Wang Xinxin & Shen Xiaopan & Ai Xueyi & Li Shijia, 2023. "Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-27, September.
  19. Minghao Ran & Yingchao Wang & Qilu Qin & Jindi Huang & Jiading Jiang, 2025. "An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power," Sustainability, MDPI, vol. 17(11), pages 1-31, May.
  20. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
  21. Wang, Yaqi & Zhao, Xiaomeng & Li, Zheng & Zhu, Wenbo & Gui, Renzhou, 2024. "A novel hybrid model for multi-step-ahead forecasting of wind speed based on univariate data feature enhancement," Energy, Elsevier, vol. 312(C).
  22. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2024. "A hybrid methodology using VMD and disentangled features for wind speed forecasting," Energy, Elsevier, vol. 288(C).
  23. Sun, Xiaoying & Liu, Haizhong, 2024. "Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S," Energy, Elsevier, vol. 305(C).
  24. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
  25. Hongtao Li & Xiaoxuan Li & Shaolong Sun & Zhipeng Huang & Xiaoyan Jia, 2024. "Multivariable forecasting approach of high‐speed railway passenger demand based on residual term of Baidu search index and error correction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2401-2433, November.
  26. Geng, Donghan & Zhang, Yongkang & Zhang, Yunlong & Qu, Xingchuang & Li, Longfei, 2025. "A hybrid model based on CapSA-VMD-ResNet-GRU-attention mechanism for ultra-short-term and short-term wind speed prediction," Renewable Energy, Elsevier, vol. 240(C).
  27. Wang, Zheng & Peng, Tian & Zhang, Xuedong & Chen, Jialei & Qian, Shijie & Zhang, Chu, 2025. "Enhancing multi-step short-term solar radiation forecasting based on optimized generalized regularized extreme learning machine and multi-scale Gaussian data augmentation technique," Applied Energy, Elsevier, vol. 377(PD).
  28. Lu Peng & Sheng‐Xiang Lv & Lin Wang, 2024. "Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2064-2087, September.
  29. Wang, Xiangzhen & Li, Yapeng & Gong, Shun & Hu, Xue & Cheng, Chuntian, 2025. "An enhanced micro-PSO method to deal with asymmetric electricity markets competition within hydropower cascade," Applied Energy, Elsevier, vol. 377(PA).
  30. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
  31. Jian Zhu & Zhiyuan Zhao & Xiaoran Zheng & Zhao An & Qingwu Guo & Zhikai Li & Jianling Sun & Yuanjun Guo, 2023. "Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer," Energies, MDPI, vol. 16(22), pages 1-15, November.
  32. Wang, Chao & Lin, Hong & Hu, Heng & Yang, Ming & Ma, Li, 2024. "A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction," Energy, Elsevier, vol. 293(C).
  33. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2023. "A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate," Sustainability, MDPI, vol. 15(24), pages 1-39, December.
  34. Duan, Zhu & Liu, Hui & Li, Ye & Nikitas, Nikolaos, 2022. "Time-variant post-processing method for long-term numerical wind speed forecasts based on multi-region recurrent graph network," Energy, Elsevier, vol. 259(C).
  35. Liang, Yang & Zhang, Dongqin & Zhang, Jize & Hu, Gang, 2024. "A state-of-the-art analysis on decomposition method for short-term wind speed forecasting using LSTM and a novel hybrid deep learning model," Energy, Elsevier, vol. 313(C).
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