Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures

Citations

Cited by:

1. Wu, Tangjie & Ling, Qiang, 2024. "STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting," Applied Energy, Elsevier, vol. 375(C).
2. Niu, Zhewen & Han, Xiaoqing & Zhang, Dongxia & Wu, Yuxiang & Lan, Songyan, 2024. "Interpretable wind power forecasting combining seasonal-trend representations learning with temporal fusion transformers architecture," Energy, Elsevier, vol. 306(C).
3. Yan Chen & Miaolin Yu & Haochong Wei & Huanxing Qi & Yiming Qin & Xiaochun Hu & Rongxing Jiang, 2025. "A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning," Energies, MDPI, vol. 18(3), pages 1-20, January.
4. 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).
5. Wu, Binrong & Wang, Lin, 2024. "Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting," Energy, Elsevier, vol. 288(C).
6. Wu Xu & Wenjing Dai & Dongyang Li & Qingchang Wu, 2024. "Short-Term Wind Power Prediction Based on a Variational Mode Decomposition–BiTCN–Psformer Hybrid Model," Energies, MDPI, vol. 17(16), pages 1-17, August.
7. Lars Ødegaard Bentsen & Narada Dilp Warakagoda & Roy Stenbro & Paal Engelstad, 2023. "A Unified Graph Formulation for Spatio-Temporal Wind Forecasting," Energies, MDPI, vol. 16(20), pages 1-23, October.
8. 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).
9. 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).
10. Wang, Shuangxin & Shi, Jiarong & Yang, Wei & Yin, Qingyan, 2024. "High and low frequency wind power prediction based on Transformer and BiGRU-Attention," Energy, Elsevier, vol. 288(C).
11. Jinhua Zhang & Hui Li & Peng Cheng & Jie Yan, 2024. "Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network," Energies, MDPI, vol. 17(2), pages 1-16, January.
12. Mo, Yipeng & Wang, Haoxin & Yang, Chengteng & Yao, Zuhua & Li, Bixiong & Fan, Songhai & Mo, Site, 2024. "FDNet: Frequency filter enhanced dual LSTM network for wind power forecasting," Energy, Elsevier, vol. 312(C).
13. Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).
14. 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).
15. 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).
16. Junhao Zhao & Xiaodong Shen & Youbo Liu & Junyong Liu & Xisheng Tang, 2024. "Enhancing Aggregate Load Forecasting Accuracy with Adversarial Graph Convolutional Imputation Network and Learnable Adjacency Matrix," Energies, MDPI, vol. 17(18), pages 1-28, September.
17. Shi, Peiming & Lin, Shengmao & Song, Dongran & Xu, Xuefang & Wu, Jie, 2024. "TRNet: A trend and residual network utilizing novel hilly attention mechanism for wind speed prediction in complex scenario," Energy, Elsevier, vol. 309(C).
18. Hongxia Wang & Xiao Jin & Jianian Wang & Hongxia Hao, 2023. "Nonparametric Estimation for High-Dimensional Space Models Based on a Deep Neural Network," Mathematics, MDPI, vol. 11(18), pages 1-37, September.
19. Dong, Xianzhou & Luo, Yongqiang & Yuan, Shuo & Tian, Zhiyong & Zhang, Limao & Wu, Xiaoying & Liu, Baobing, 2025. "Building electricity load forecasting based on spatiotemporal correlation and electricity consumption behavior information," Applied Energy, Elsevier, vol. 377(PB).
20. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
21. Wu, Xinning & Zhan, Haolin & Hu, Jianming & Wang, Ying, 2025. "Non-stationary GNNCrossformer: Transformer with graph information for non-stationary multivariate Spatio-Temporal wind power data forecasting," Applied Energy, Elsevier, vol. 377(PB).
22. Elshafei, Basem & Popov, Atanas & Giddings, Donald, 2024. "Enhanced offshore wind resource assessment using hybrid data fusion and numerical models," Energy, Elsevier, vol. 310(C).
23. Zhang, Dongqin & Hu, Gang & Song, Jie & Gao, Huanxiang & Ren, Hehe & Chen, Wenli, 2024. "A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model," Energy, Elsevier, vol. 288(C).
24. Xu, Xuefang & Hu, Shiting & Shao, Huaishuang & Shi, Peiming & Li, Ruixiong & Li, Deguang, 2023. "A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm," Energy, Elsevier, vol. 284(C).
25. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).
26. Xu, Ningke & Li, Shuang & Xu, Kun & Lu, Cheng, 2025. "Research on methane Hazard interval prediction method based on hybrid “model-data”driven strategy," Applied Energy, Elsevier, vol. 377(PC).