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Wind power forecasting – A data-driven method along with gated recurrent neural network

Citations

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Cited by:

  1. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt & Tomasz Gulczyński, 2022. "Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms," Energies, MDPI, vol. 15(4), pages 1-30, February.
  2. Jie Du & Shuaizhi Chen & Linlin Pan & Yubao Liu, 2025. "A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model," Energies, MDPI, vol. 18(5), pages 1-26, February.
  3. Jiawei Zhang & Rongquan Zhang & Yanfeng Zhao & Jing Qiu & Siqi Bu & Yuxiang Zhu & Gangqiang Li, 2023. "Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model," Energies, MDPI, vol. 16(10), pages 1-15, May.
  4. Zhiyong Guo & Fangzheng Wei & Wenkai Qi & Qiaoli Han & Huiyuan Liu & Xiaomei Feng & Minghui Zhang, 2024. "A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network," Sustainability, MDPI, vol. 16(8), pages 1-20, April.
  5. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
  6. Meng, Anbo & Li, Hanhong & Yin, Hao & Li, Xuecong, 2025. "A novel net load prediction approach using multi-scale deep learning network based on Trend-Multi-Period encoder and segment imbalance regression," Energy, Elsevier, vol. 332(C).
  7. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
  8. Zifa Liu & Xinyi Li & Haiyan Zhao, 2023. "Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction," Energies, MDPI, vol. 16(10), pages 1-24, May.
  9. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
  10. Zhao, Wanbing & Chang, Weiguang & Yang, Qiang, 2024. "Collaborative energy management of interconnected regional integrated energy systems considering spatio-temporal characteristics," Renewable Energy, Elsevier, vol. 235(C).
  11. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
  12. Liu, Lei & Liu, Jicheng & Ye, Yu & Liu, Hui & Chen, Kun & Li, Dong & Dong, Xue & Sun, Mingzhai, 2023. "Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty," Renewable Energy, Elsevier, vol. 205(C), pages 598-607.
  13. Jin, Huaiping & Yang, Guanzhi & Dong, Shoulong & Fan, Shouyuan & Jin, Huaikang & Wang, Bin, 2025. "Wind power forecasting for newly built wind farms based on deep learning with dual-stage attention mechanism and adaptive transfer learning," Energy, Elsevier, vol. 335(C).
  14. Ruslan Abdulkadirov & Pavel Lyakhov & Nikolay Nagornov, 2023. "Survey of Optimization Algorithms in Modern Neural Networks," Mathematics, MDPI, vol. 11(11), pages 1-37, May.
  15. Zhong, Lingshu & Wu, Pan & Pei, Mingyang, 2024. "Wind power generation prediction during the COVID-19 epidemic based on novel hybrid deep learning techniques," Renewable Energy, Elsevier, vol. 222(C).
  16. Gao, Jiaxin & Cheng, Yuanqi & Zhang, Dongxiao & Chen, Yuntian, 2025. "Physics-constrained wind power forecasting aligned with probability distributions for noise-resilient deep learning," Applied Energy, Elsevier, vol. 383(C).
  17. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
  18. Zhou, Gaoyu & Hu, Guofeng & Zhang, Daxing & Zhang, Yun, 2023. "A novel algorithm system for wind power prediction based on RANSAC data screening and Seq2Seq-Attention-BiGRU model," Energy, Elsevier, vol. 283(C).
  19. Guo, Yi & Ming, Bo & Huang, Qiang & Wang, Yimin & Zheng, Xudong & Zhang, Wei, 2022. "Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery," Applied Energy, Elsevier, vol. 309(C).
  20. Ge, Chang & Yan, Jie & Song, Weiye & Zhang, Haoran & Wang, Han & Li, Yuhao & Liu, Yongqian, 2025. "Middle-term wind power forecasting method based on long-span NWP and microscale terrain fusion correction," Renewable Energy, Elsevier, vol. 240(C).
  21. Jin, Huaiping & Zhang, Kehao & Fan, Shouyuan & Jin, Huaikang & Wang, Bin, 2024. "Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning," Energy, Elsevier, vol. 308(C).
  22. Yin, Hao & Yin, Yiding & Li, Hanhong & Zhu, Jianbin & Xian, Zikang & Tang, Yanshu & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhang, Haitao & Xie, Zhifeng & Meng, Anbo, 2025. "Carbon emissions trading price forecasting based on temporal-spatial multidimensional collaborative attention network and segment imbalance regression," Applied Energy, Elsevier, vol. 377(PA).
  23. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
  24. Xiaoshuang Huang & Yinbao Zhang & Jianzhong Liu & Xinjia Zhang & Sicong Liu, 2023. "A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit," Sustainability, MDPI, vol. 15(19), pages 1-13, September.
  25. Md. Ahasan Habib & M. J. Hossain, 2024. "Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering," Energies, MDPI, vol. 17(5), pages 1-23, March.
  26. 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).
  27. Fan Cai & Dongdong Chen & Yuesong Jiang & Tongbo Zhu, 2024. "Short-Term Wind Power Forecasting Based on OMNIC and Adaptive Fractional Order Generalized Pareto Motion Model," Energies, MDPI, vol. 17(23), pages 1-20, November.
  28. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
  29. Dongyu Wang & Xiwen Cui & Dongxiao Niu, 2022. "Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
  30. Karijadi, Irene & Chou, Shuo-Yan & Dewabharata, Anindhita, 2023. "Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method," Renewable Energy, Elsevier, vol. 218(C).
  31. Fu, Zhengze & Qian, Hongliang & Wei, Wei & Chu, Xuanxuan & Yang, Fan & Guo, Chengchao & Wang, Fuming, 2025. "An Informer-BiGRU-temporal attention multi-step wind speed prediction model based on spatial-temporal dimension denoising and combined VMD decomposition," Energy, Elsevier, vol. 326(C).
  32. Liu, Lei & Wang, Xinyu & Dong, Xue & Chen, Kang & Chen, Qiuju & Li, Bin, 2024. "Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series," Applied Energy, Elsevier, vol. 374(C).
  33. Zhang, Wanqing & Lin, Zi & Liu, Xiaolei, 2022. "Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)," Renewable Energy, Elsevier, vol. 185(C), pages 611-628.
  34. 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).
  35. Yang, Shixi & Zhou, Jiaxuan & Gu, Xiwen & Mei, Yiming & Duan, Jiangman, 2024. "A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application," Energy, Elsevier, vol. 313(C).
  36. Wang, Lei & He, Yigang, 2022. "M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions," Applied Energy, Elsevier, vol. 324(C).
  37. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
  38. Upma Singh & Mohammad Rizwan & Hasmat Malik & Fausto Pedro García Márquez, 2022. "Wind Energy Scenario, Success and Initiatives towards Renewable Energy in India—A Review," Energies, MDPI, vol. 15(6), pages 1-39, March.
  39. Hanifi, Shahram & Zare-Behtash, Hossein & Cammarano, Andrea & Lotfian, Saeid, 2023. "Offshore wind power forecasting based on WPD and optimised deep learning methods," Renewable Energy, Elsevier, vol. 218(C).
  40. Aisha Blfgeh & Hanadi Alkhudhayr, 2024. "A Machine Learning-Based Sustainable Energy Management of Wind Farms Using Bayesian Recurrent Neural Network," Sustainability, MDPI, vol. 16(19), pages 1-21, September.
  41. Wang, Yonggang & Zhao, Kaixing & Hao, Yue & Yao, Yilin, 2024. "Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory," Applied Energy, Elsevier, vol. 366(C).
  42. Zhang, Can & Xiao, Xianyong & Wang, Ying & Hou, Michael Z. & Huang, Shudong & Hu, Wenxi & Hu, Ming & Huang, Rui, 2025. "Fine-grained ultra-short-term wind power forecasting based on Temporal Fusion Transformers integrated with turbine power time series clustering," Energy, Elsevier, vol. 335(C).
  43. Ge, Chang & Yan, Jie & Zhang, Haoran & Li, Yuhao & Wang, Han & Liu, Yongqian, 2024. "Joint short-term power forecasting of hydro-wind-photovoltaic considering spatiotemporal delay of weather processes," Renewable Energy, Elsevier, vol. 237(PB).
  44. Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
  45. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  46. Guo, Yi & Ming, Bo & Huang, Qiang & Liu, Pan & Wang, Yimin & Fang, Wei & Zhang, Wei, 2022. "Evaluating effects of battery storage on day-ahead generation scheduling of large hydro–wind–photovoltaic complementary systems," Applied Energy, Elsevier, vol. 324(C).
  47. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
  48. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
  49. 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).
  50. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  51. Fudong Li & Yongjun Gan & Xiaolong Li, 2025. "The Application of Wind Power Prediction Based on the NGBoost–GRU Fusion Model in Traffic Renewable Energy System," Sustainability, MDPI, vol. 17(14), pages 1-28, July.
  52. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
  53. Gu, Bingmei & Liu, Jiaguo & Ye, Xiaoheng & Gong, Yu & Chen, Jihong, 2024. "Data-driven approach for port resilience evaluation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
  54. Liu, Tianhong & Qi, Shengli & Qiao, Xianzhu & Liu, Sixing, 2024. "A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network," Energy, Elsevier, vol. 288(C).
  55. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
  56. Niu, Dongxiao & Sun, Lijie & Yu, Min & Wang, Keke, 2022. "Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model," Energy, Elsevier, vol. 254(PA).
  57. Du, Ruoyun & Chen, Hongfei & Yu, Min & Li, Wanying & Niu, Dongxiao & Wang, Keke & Zhang, Zuozhong, 2024. "3DTCN-CBAM-LSTM short-term power multi-step prediction model for offshore wind power based on data space and multi-field cluster spatio-temporal correlation," Applied Energy, Elsevier, vol. 376(PA).
  58. Han, Yixiao & Liao, Yanfen & Ma, Xiaoqian & Guo, Xing & Li, Changxin & Liu, Xinyu, 2023. "Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network," Renewable Energy, Elsevier, vol. 215(C).
  59. Honghai Niu & Yu Yang & Lingchao Zeng & Yiguo Li, 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power," Energies, MDPI, vol. 14(3), pages 1-15, January.
  60. Meng, Anbo & Chen, Shun & Ou, Zuhong & Ding, Weifeng & Zhou, Huaming & Fan, Jingmin & Yin, Hao, 2022. "A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization," Energy, Elsevier, vol. 238(PB).
  61. Xing, Zhikai & He, Yigang, 2023. "Multi-modal multi-step wind power forecasting based on stacking deep learning model," Renewable Energy, Elsevier, vol. 215(C).
  62. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
  63. Alexandra-Nicoleta Ciucu-Durnoi & Margareta Stela Florescu & Camelia Delcea, 2023. "Envisioning Romania’s Path to Sustainable Development: A Prognostic Approach," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
  64. Ye, Xiaoling & Liu, Chengcheng & Xiong, Xiong & Qi, Yinyi, 2025. "Recurrent attention encoder–decoder network for multi-step interval wind power prediction," Energy, Elsevier, vol. 315(C).
  65. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
  66. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
  67. Hanifi, Shahram & Cammarono, Andrea & Zare-Behtash, Hossein, 2024. "Advanced hyperparameter optimization of deep learning models for wind power prediction," Renewable Energy, Elsevier, vol. 221(C).
  68. Trizoglou, Pavlos & Liu, Xiaolei & Lin, Zi, 2021. "Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines," Renewable Energy, Elsevier, vol. 179(C), pages 945-962.
  69. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
  70. Mirza, Adeel Feroz & Shu, Zhaokun & Usman, Muhammad & Mansoor, Majad & Ling, Qiang, 2024. "Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction," Renewable Energy, Elsevier, vol. 220(C).
  71. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
  72. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
  73. Dong, Yunxuan & Zhou, Binggui & Zhang, Hongcai & Yang, Guanghua & Ma, Shaodan, 2026. "A deep time–frequency augmented wind power forecasting model," Renewable Energy, Elsevier, vol. 256(PA).
  74. Gao, Peng & Yang, Yang & Li, Fei & Ge, Jiaxin & Yin, Qianqian & Wang, Ruikun, 2024. "Research on integrated decision making of multiple load combination forecasting for integrated energy system," Energy, Elsevier, vol. 311(C).
  75. Sobolewski, Robert Adam & Tchakorom, Médane & Couturier, Raphaël, 2023. "Gradient boosting-based approach for short- and medium-term wind turbine output power prediction," Renewable Energy, Elsevier, vol. 203(C), pages 142-160.
  76. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
  77. Fang, Lei & He, Bin & Yu, Sheng, 2025. "A modular multi-step forecasting method for offshore wind power clusters," Applied Energy, Elsevier, vol. 380(C).
  78. Srinivas, Gollapalli Veera Satya & Rajesh, M.V., 2026. "Spatial-temporal modeling with DL-based hybrid cheetah hippopotamus optimizer framework for wind energy forecasting and turbine performance enhancement," Renewable Energy, Elsevier, vol. 256(PA).
  79. Xiao, Yiping & Wei, Honghao & Shi, Ying & Zhang, Haiyang & Shen, Zongtao & Jiao, Hongjian, 2025. "A short-term wind power prediction based on MCOOT optimized deep learning networks and attention-weighted environmental factors for error correction," Energy, Elsevier, vol. 324(C).
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