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Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression

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  1. Guanjun Liu & Chao Wang & Hui Qin & Jialong Fu & Qin Shen, 2022. "A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting," Energies, MDPI, vol. 15(19), pages 1-16, September.
  2. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
  3. Zhaoshuang He & Yanhua Chen & Yale Zang, 2024. "Wind Speed Forecasting Based on Phase Space Reconstruction and a Novel Optimization Algorithm," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
  4. Xiang Ying & Keke Zhao & Zhiqiang Liu & Jie Gao & Dongxiao He & Xuewei Li & Wei Xiong, 2022. "Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
  5. Liu, Guanjun & Qin, Hui & Shen, Qin & Lyv, Hao & Qu, Yuhua & Fu, Jialong & Liu, Yongqi & Zhou, Jianzhong, 2021. "Probabilistic spatiotemporal solar irradiation forecasting using deep ensembles convolutional shared weight long short-term memory network," Applied Energy, Elsevier, vol. 300(C).
  6. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
  7. Dong, Xing & Zhang, Chenghui & Sun, Bo, 2022. "Optimization strategy based on robust model predictive control for RES-CCHP system under multiple uncertainties," Applied Energy, Elsevier, vol. 325(C).
  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. Zhang, Zhendong & Dai, Huichao & Jiang, Dingguo & Yu, Yi & Tian, Rui, 2024. "Multi-step ahead forecasting of wind vector for multiple wind turbines based on new deep learning model," Energy, Elsevier, vol. 304(C).
  10. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
  11. Xin Zhao & Haikun Wei & Chenxi Li & Kanjian Zhang, 2020. "A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed," Energies, MDPI, vol. 13(7), pages 1-15, April.
  12. 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).
  13. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
  14. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  15. Bommidi, Bala Saibabu & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function," Energy, Elsevier, vol. 265(C).
  16. Wang, Cong & He, Yan & Zhang, Hong-li & Ma, Ping, 2024. "Wind power forecasting based on manifold learning and a double-layer SWLSTM model," Energy, Elsevier, vol. 290(C).
  17. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  18. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Li, Wenzhe & Li, Fei & Lee, Jay, 2021. "A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 178(C), pages 709-719.
  19. Zhang, Wanying & He, Yaoyao & Yang, Shanlin, 2023. "A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind power," Renewable Energy, Elsevier, vol. 202(C), pages 992-1011.
  20. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  21. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
  22. Zhang, Yue & Wang, Yeqin & Zhang, Chu & Qiao, Xiujie & Ge, Yida & Li, Xi & Peng, Tian & Nazir, Muhammad Shahzad, 2024. "State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural netw," Applied Energy, Elsevier, vol. 356(C).
  23. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(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. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
  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. Xu, Xuefang & Hu, Shiting & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong & Li, Zhi, 2023. "Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm," Energy, Elsevier, vol. 262(PA).
  28. Mkono, Christopher N. & Chuanbo, Shen & Mulashani, Alvin K. & Mwakipunda, Grant Charles, 2023. "Deep learning integrated approach for hydrocarbon source rock evaluation and geochemical indicators prediction in the Jurassic - Paleogene of the Mandawa basin, SE Tanzania," Energy, Elsevier, vol. 284(C).
  29. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
  30. Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
  31. Liu, Yi & Wang, Ranpeng & Gu, Yin & Li, Congjian & Wang, Gangqiao, 2024. "Physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction with experimental validation," Energy, Elsevier, vol. 298(C).
  32. Xiao, Wenjing & Mo, Li & Xu, Zhanxing & Liu, Chang & Zhang, Yongchuan, 2024. "A hybrid electric load forecasting model based on decomposition considering fisher information," Applied Energy, Elsevier, vol. 364(C).
  33. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
  34. 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.
  35. Che, Zhongyuan & Peng, Chong & Yue, Chenxiao, 2024. "Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
  36. Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
  37. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).
  38. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
  39. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
  40. Yang, Yang & Lang, Jin & Wu, Jian & Zhang, Yanyan & Su, Lijie & Song, Xiangman, 2022. "Wind speed forecasting with correlation network pruning and augmentation: A two-phase deep learning method," Renewable Energy, Elsevier, vol. 198(C), pages 267-282.
  41. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
  42. 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).
  43. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
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