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Short-term wind speed forecasting using recurrent neural networks with error correction

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Cited by:

  1. Huu Khoa Minh Nguyen & Quoc-Dung Phan & Yuan-Kang Wu & Quoc-Thang Phan, 2023. "Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)," Energies, MDPI, vol. 16(9), pages 1-20, April.
  2. Xiao, Xun & Mo, Huadong & Zhang, Yinan & Shan, Guangcun, 2022. "Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting," Energy, Elsevier, vol. 246(C).
  3. Qingyuan Wang & Longnv Huang & Jiehui Huang & Qiaoan Liu & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
  4. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
  5. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
  6. Sergey Obukhov & Emad M. Ahmed & Denis Y. Davydov & Talal Alharbi & Ahmed Ibrahim & Ziad M. Ali, 2021. "Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process," Energies, MDPI, vol. 14(17), pages 1-15, September.
  7. Tian, Zhongda & Chen, Hao, 2021. "Multi-step short-term wind speed prediction based on integrated multi-model fusion," Applied Energy, Elsevier, vol. 298(C).
  8. Mazzeo, Domenico & Herdem, Münür Sacit & Matera, Nicoletta & Bonini, Matteo & Wen, John Z. & Nathwani, Jatin & Oliveti, Giuseppe, 2021. "Artificial intelligence application for the performance prediction of a clean energy community," Energy, Elsevier, vol. 232(C).
  9. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
  10. Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  11. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
  12. Xiong, Xiong & Zou, Ruilin & Sheng, Tao & Zeng, Weilin & Ye, Xiaoling, 2023. "An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed," Energy, Elsevier, vol. 283(C).
  13. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
  14. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
  15. Wang, Xuguang & Ren, Huan & Zhai, Junhai & Xing, Hongjie & Su, Jie, 2022. "Adaptive support segment based short-term wind speed forecasting," Energy, Elsevier, vol. 249(C).
  16. Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
  17. Yaqi Wang & Renzhou Gui, 2022. "A Hybrid Model for GRU Ultra-Short-Term Wind Speed Prediction Based on Tsfresh and Sparse PCA," Energies, MDPI, vol. 15(20), pages 1-20, October.
  18. Yan, Bowen & Shen, Ruifang & Li, Ke & Wang, Zhenguo & Yang, Qingshan & Zhou, Xuhong & Zhang, Le, 2023. "Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations," Energy, Elsevier, vol. 284(C).
  19. Zhou, Jianguo & Xu, Zhongtian, 2023. "A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices," Resources Policy, Elsevier, vol. 80(C).
  20. Wu, Jie & Li, Na & Zhao, Yan & Wang, Jujie, 2022. "Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting," Energy, Elsevier, vol. 242(C).
  21. 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.
  22. Yang, Rui & Liu, Hui & Nikitas, Nikolaos & Duan, Zhu & Li, Yanfei & Li, Ye, 2022. "Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach," Energy, Elsevier, vol. 239(PB).
  23. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
  24. Yu, Chuanjin & Li, Yongle & Chen, Qian & Lai, Xiaopan & Zhao, Liyang, 2022. "Matrix-based wavelet transformation embedded in recurrent neural networks for wind speed prediction," Applied Energy, Elsevier, vol. 324(C).
  25. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
  26. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
  27. Duan, Jikai & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Zuo, Hongchao & Bai, Yulong & Chen, Bolong, 2022. "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Elsevier, vol. 200(C), pages 788-808.
  28. Wang, Ying & Wang, Jianzhou & Li, Zhiwu & Yang, Hufang & Li, Hongmin, 2021. "Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction," Energy, Elsevier, vol. 231(C).
  29. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
  30. Qingsheng Zhao & Juwen Mu & Xiaoqing Han & Dingkang Liang & Xuping Wang, 2021. "Evaluation Model of Operation State Based on Deep Learning for Smart Meter," Energies, MDPI, vol. 14(15), pages 1-17, August.
  31. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
  32. Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.
  33. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2024. "A hybrid methodology using VMD and disentangled features for wind speed forecasting," Energy, Elsevier, vol. 288(C).
  34. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
  35. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
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