IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v323y2022ics0306261922009138.html

A hybrid attention-based deep learning approach for wind power prediction

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Li, Tian & Ai, Lijuan & Yang, Qingshan & Zhang, Xingxin & Li, Hang & Lu, Dawei & Shen, Hongtao, 2025. "Short-term wind power prediction based on multiscale numerical simulation coupled with deep learning," Renewable Energy, Elsevier, vol. 246(C).
  2. Talaat, Fatma M. & Kabeel, A.E. & Shaban, Warda M., 2024. "The role of utilizing artificial intelligence and renewable energy in reaching sustainable development goals," Renewable Energy, Elsevier, vol. 235(C).
  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. Yang, Mao & Wang, Da & Zhang, Wei, 2024. "A novel ultra short-term wind power prediction model based on double model coordination switching mechanism," Energy, Elsevier, vol. 289(C).
  5. Qiu, Hong & Shi, Kaikai & Wang, Renfang & Zhang, Liang & Liu, Xiufeng & Cheng, Xu, 2024. "A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects," Renewable Energy, Elsevier, vol. 227(C).
  6. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
  7. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
  8. Wan, Hang & Wang, Jiasong & Gan, Quan & Xia, Yaping & Chang, Yufang & Yan, Huaicheng, 2025. "Addressing intermittency in medium-term photovoltaic and wind power forecasting using a hybrid xLSTM-TCCNN model with numerical weather predictions," Renewable Energy, Elsevier, vol. 253(C).
  9. Zhang, Dongjie & Mei, Yong & Wang, Xiaoyang & Wang, Zheng & Cheng, Yun & Zhan, Changhong, 2026. "Heat loss identification method for heating building exterior walls based on BO-TCN-LSTM-SA," Energy, Elsevier, vol. 342(C).
  10. Sheng Huang & Huakun Que & Lukun Zeng & Jingxu Yang & Kaihong Zheng, 2024. "Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction," Energies, MDPI, vol. 17(23), pages 1-18, November.
  11. Liu, Shuhan & Sun, Wenqiang, 2025. "Knowledge- and data-driven prediction of blast furnace gas generation and consumption in iron and steel sites," Applied Energy, Elsevier, vol. 390(C).
  12. 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.
  13. Zheng, Xidong & Bai, Feifei & Zhuang, Zhiyuan & Chen, Zixing & Jin, Tao, 2023. "A new demand response management strategy considering renewable energy prediction and filtering technology," Renewable Energy, Elsevier, vol. 211(C), pages 656-668.
  14. 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.
  15. Yin, Linfei & Wang, Nannan & Li, Jishen, 2025. "Electricity terminal multi-label recognition with a “one-versus-all” rejection recognition algorithm based on adaptive distillation increment learning and attention MobileNetV2 network for non-invasive load monitoring," Applied Energy, Elsevier, vol. 382(C).
  16. Li, Ke & Mu, Yuchen & Yang, Fan & Wang, Haiyang & Yan, Yi & Zhang, Chenghui, 2024. "Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism," Applied Energy, Elsevier, vol. 360(C).
  17. Gao, Huanxiang & Hu, Gang & Zhang, Dongqin & Jiang, Wenjun & Ren, Hehe & Chen, Wenli, 2024. "Prediction of wind fields in mountains at multiple elevations using deep learning models," Applied Energy, Elsevier, vol. 353(PA).
  18. Colak, Zeynep, 2026. "Hybrid machine learning approach in forecasting renewable energy production: Extra trees, catBoost and LightGBM based stacking model," Renewable Energy, Elsevier, vol. 260(C).
  19. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
  20. Li, Huanhuan & Zhang, Yu & Li, Yan & Lam, Jasmine Siu Lee & Matthews, Christian & Yang, Zaili, 2025. "Deep multi-view information-powered vessel traffic flow prediction for intelligent transportation management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
  21. Zhang, Zeguo & Yin, Jianchuan, 2025. "A novel depthwise separable U-Net for large-scale wave field prediction," Renewable Energy, Elsevier, vol. 254(C).
  22. Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
  23. Deng, Jiewen & Xiao, Zhao & Zhao, Qiancheng & Zhan, Jun & Tao, Jie & Liu, Minghua & Song, Dongran, 2024. "Wind turbine short-term power forecasting method based on hybrid probabilistic neural network," Energy, Elsevier, vol. 313(C).
  24. Sahu, Nepal & Azad, Chandrashekhar & Kumar, Uday, 2025. "Interpretable and highly accurate tertiary tree-based ensemble hybrid models for the prediction of photocurrent density and electrode potential in PEC cell: Theoretically supported and externally validated by experimental data," Applied Energy, Elsevier, vol. 401(PB).
  25. Zhang, Zeguo & Yin, Jianchuan, 2025. "Incremental principal component analysis based depthwise separable Unet model for complex wind system forecasting," Energy, Elsevier, vol. 334(C).
  26. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
  27. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  28. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
  29. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
  30. Sarıman, Güncel & Keçebaş, Ali, 2026. "Global renewable energy forecasting using hybrid ML/DL models: Economic and geospatial insights," Energy Policy, Elsevier, vol. 208(C).
  31. Na Fang & Zhengguang Liu & Shilei Fan, 2025. "Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model," Energies, MDPI, vol. 18(6), pages 1-18, March.
  32. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
  33. Li, Ke & Qin, Zheng & Mu, Yuchen & Wang, Haiyang & Bie, Qingfeng & Yin, Xianxin & Yan, Yi, 2025. "Transfer learning-based multi-energy load forecasting method for integrated energy system with zero-shot," Applied Energy, Elsevier, vol. 401(PC).
  34. Agnieszka Dudziak & Arkadiusz Małek & Andrzej Marciniak & Jacek Caban & Jarosław Seńko, 2024. "Probabilistic Analysis of Green Hydrogen Production from a Mix of Solar and Wind Energy," Energies, MDPI, vol. 17(17), pages 1-22, September.
  35. Rathod, Deepak & Gidwani, Lata, 2026. "A literature review based on density forecasting and uncertainty quantification of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
  36. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
  37. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
  38. Li, Yanhui & Sun, Kaixuan & Yao, Qi & Wang, Lin, 2024. "A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm," Energy, Elsevier, vol. 286(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.