IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v199y2022icp977-992.html

Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks

Citations

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


Cited by:

  1. 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).
  2. Chen, Zhengganzhe & Zhang, Bin & Du, Chenglong & Meng, Wei & Meng, Anbo, 2024. "A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction," Energy, Elsevier, vol. 294(C).
  3. Agnieszka Sompolska-Rzechuła & Iwona Bąk & Aneta Becker & Henryk Marjak & Joanna Perzyńska, 2024. "The Use of Renewable Energy Sources and Environmental Degradation in EU Countries," Sustainability, MDPI, vol. 16(23), pages 1-32, November.
  4. 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).
  5. Wu, Zheng & Zhang, Yue & Dong, Ze, 2024. "NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants," Energy, Elsevier, vol. 305(C).
  6. Jiang, Wenjun & Liu, Bo & Liang, Yang & Gao, Huanxiang & Lin, Pengfei & Zhang, Dongqin & Hu, Gang, 2024. "Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables," Applied Energy, Elsevier, vol. 353(PB).
  7. Wen-Dar Guo & Wei-Bo Chen & Chih-Hsin Chang, 2025. "A spatiotemporal watershed-scale machine-learning model for hourly and daily flood-water level prediction: the case of the tidal Beigang River, Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(8), pages 9563-9611, May.
  8. Antonesi, Gabriel & Cioara, Tudor & Anghel, Ionut & Michalakopoulos, Vasilis & Sarmas, Elissaios & Toderean, Liana, 2025. "A systematic review of transformers and large language models in the energy sector: towards agentic digital twins," Applied Energy, Elsevier, vol. 401(PA).
  9. Verdone, Alessio & Panella, Massimo & De Santis, Enrico & Rizzi, Antonello, 2025. "A review of solar and wind energy forecasting: From single-site to multi-site paradigm," Applied Energy, Elsevier, vol. 392(C).
  10. Zongwei Zhang & Lianlei Lin & Sheng Gao & Junkai Wang & Hanqing Zhao & Hangyi Yu, 2025. "A machine learning model for hub-height short-term wind speed prediction," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
  11. Dai, Junfeng & Fu, Li-hui, 2024. "A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm," Energy, Elsevier, vol. 298(C).
  12. Oliveira Santos, Victor & Costa Rocha, Paulo Alexandre & Scott, John & Van Griensven Thé, Jesse & Gharabaghi, Bahram, 2023. "Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database," Energy, Elsevier, vol. 278(PA).
  13. 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).
  14. 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.
  15. Wang, Zhijin & Liu, Xiufeng & Huang, Yaohui & Zhang, Peisong & Fu, Yonggang, 2023. "A multivariate time series graph neural network for district heat load forecasting," Energy, Elsevier, vol. 278(PA).
  16. Ashkan Safari & Hamed Kheirandish Gharehbagh & Morteza Nazari Heris, 2023. "DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm," Energies, MDPI, vol. 16(19), pages 1-22, September.
  17. Wu, Tangjie & Ling, Qiang, 2024. "Self-supervised dynamic stochastic graph network for spatio-temporal wind speed forecasting," Energy, Elsevier, vol. 304(C).
  18. Chen, Yunxiao & Lin, Chaojing & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2024. "Proactive failure warning for wind power forecast models based on volatility indicators analysis," Energy, Elsevier, vol. 305(C).
  19. Chen, Xin & Ye, Xiaoling & Shi, Jian & Zhang, Yingchao & Xiong, Xiong, 2024. "A spatial transfer-based hybrid model for wind speed forecasting," Energy, Elsevier, vol. 313(C).
  20. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
  21. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(C).
  22. 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.
  23. Leng, Zhiyuan & Chen, Lu & Yi, Bin & Liu, Fanqian & Xie, Tao & Mei, Ziyi, 2025. "Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition," Energy, Elsevier, vol. 322(C).
  24. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
  25. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
  26. Wang, Li & Gao, Jinhan & Li, Yunchao & Wang, Da, 2025. "A method for ultra-short-term wind power forecasting of large-scale wind farms based on adaptive spatiotemporal graph convolution," Renewable Energy, Elsevier, vol. 249(C).
  27. Wang, Chao & Lin, Hong & Yang, Ming & Fu, Xiaoling & Yuan, Yue & Wang, Zewei, 2024. "A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
  28. Qingquan Lv & Jialin Zhang & Jianmei Zhang & Zhenzhen Zhang & Qiang Zhou & Pengfei Gao & Haozhe Zhang, 2025. "Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM," Energies, MDPI, vol. 18(13), pages 1-18, June.
  29. Qu, Kai & Xue, Shuangsi & Zheng, Xiaodong & Yan, Dapeng & Cao, Hui, 2026. "Learning dynamic inter-farm dependencies for wind power forecasting via adaptive sparse graph attention network," Renewable Energy, Elsevier, vol. 258(C).
  30. Wang, Yufeng & Yang, Zihan & Ma, Jianhua & Jin, Qun, 2024. "A wind speed forecasting framework for multiple turbines based on adaptive gate mechanism enhanced multi-graph attention networks," Applied Energy, Elsevier, vol. 372(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.