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Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

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  1. Yonggang Li & Yue Wang & Binyuan Wu, 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting," Energies, MDPI, vol. 13(18), pages 1-15, September.
  2. Julio César Cuenca Tinitana & Carlos Adrian Correa-Florez & Diego Patino & José Vuelvas, 2020. "Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty," Energies, MDPI, vol. 13(23), pages 1-26, December.
  3. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
  4. Ren, Yuting & Li, Zhuolin & Xu, Lingyu & Yu, Jie, 2023. "The data-based adaptive graph learning network for analysis and prediction of offshore wind speed," Energy, Elsevier, vol. 267(C).
  5. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
  6. Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
  7. Xu, Yuanyuan & Yang, Genke & Luo, Jiliang & He, Jianan & Sun, Haixin, 2022. "A multi-location short-term wind speed prediction model based on spatiotemporal joint learning," Renewable Energy, Elsevier, vol. 183(C), pages 148-159.
  8. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
  9. Mumin Zhang & Yuzhi Wang & Haochen Zhang & Zhiyun Peng & Junjie Tang, 2023. "A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
  10. Arnas Uselis & Mantas Lukoševičius & Lukas Stasytis, 2020. "Localized Convolutional Neural Networks for Geospatial Wind Forecasting," Energies, MDPI, vol. 13(13), pages 1-21, July.
  11. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(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. Pan, Xiaoxin & Wang, Long & Wang, Zhongju & Huang, Chao, 2022. "Short-term wind speed forecasting based on spatial-temporal graph transformer networks," Energy, Elsevier, vol. 253(C).
  14. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
  15. Jizhong Xue & Zaohui Kang & Chun Sing Lai & Yu Wang & Fangyuan Xu & Haoliang Yuan, 2023. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)," Energies, MDPI, vol. 16(11), pages 1-18, May.
  16. Liang, Tao & Chai, Chunjie & Sun, Hexu & Tan, Jianxin, 2022. "Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC," Energy, Elsevier, vol. 250(C).
  17. 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).
  18. Rafael E. Carrillo & Martin Leblanc & Baptiste Schubnel & Renaud Langou & Cyril Topfel & Pierre-Jean Alet, 2020. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution," Energies, MDPI, vol. 13(21), pages 1-17, November.
  19. Manaf Ahmed & Véronique Maume‐Deschamps & Pierre Ribereau, 2022. "Recognizing a spatial extreme dependence structure: A deep learning approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
  20. Ziyu Bai & Guoqiang Sun & Haixiang Zang & Ming Zhang & Peifeng Shen & Yi Liu & Zhinong Wei, 2019. "Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China," Energies, MDPI, vol. 12(17), pages 1-19, August.
  21. Mahsa Farahani & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Soo-Mi Choi, 2023. "A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes," Sustainability, MDPI, vol. 15(13), pages 1-30, July.
  22. Zheng, Ling & Zhou, Bin & Or, Siu Wing & Cao, Yijia & Wang, Huaizhi & Li, Yong & Chan, Ka Wing, 2021. "Spatio-temporal wind speed prediction of multiple wind farms using capsule network," Renewable Energy, Elsevier, vol. 175(C), pages 718-730.
  23. Jaeik Jeong & Hongseok Kim, 2019. "Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network," Energies, MDPI, vol. 12(23), pages 1-14, November.
  24. Shamsi, Meisam & Babazadeh, Reza, 2022. "Estimation and prediction of Jatropha cultivation areas in China and India," Renewable Energy, Elsevier, vol. 183(C), pages 548-560.
  25. Baïle, Rachel & Muzy, Jean-François, 2023. "Leveraging data from nearby stations to improve short-term wind speed forecasts," Energy, Elsevier, vol. 263(PA).
  26. Hongyu Li & Ping Ju & Chun Gan & Feng Wu & Yichen Zhou & Zhe Dong, 2018. "Stochastic Stability Analysis of the Power System with Losses," Energies, MDPI, vol. 11(3), pages 1-11, March.
  27. Lagomarsino-Oneto, Daniele & Meanti, Giacomo & Pagliana, Nicolò & Verri, Alessandro & Mazzino, Andrea & Rosasco, Lorenzo & Seminara, Agnese, 2023. "Physics informed machine learning for wind speed prediction," Energy, Elsevier, vol. 268(C).
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