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Gaussian Process Regression for numerical wind speed prediction enhancement

Citations

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Cited by:

  1. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
  2. Li, Wenzhe & Jia, Xiaodong & Li, Xiang & Wang, Yinglu & Lee, Jay, 2021. "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, Elsevier, vol. 164(C), pages 242-253.
  3. Fu, Wenlong & Fu, Yuchen & Li, Bailing & Zhang, Hairong & Zhang, Xuanrui & Liu, Jiarui, 2023. "A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 348(C).
  4. Wang, Jujie & Shu, Shuqin & Xu, Shulian, 2025. "A point-interval wind speed prediction model based on entropy clustering and hybrid optimization weighted strategy," Renewable Energy, Elsevier, vol. 244(C).
  5. 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).
  6. Tan, Bendong & Su, Tong & Weng, Yu & Ye, Ketian & Pareek, Parikshit & Vorobev, Petr & Nguyen, Hung & Zhao, Junbo & Deka, Deepjyoti, 2026. "Gaussian processes in power systems: Techniques, applications, and future works," Applied Energy, Elsevier, vol. 402(PC).
  7. Yang, Mao & Guo, Yunfeng & Huang, Tao & Zhang, Wei, 2025. "Power prediction considering NWP wind speed error tolerability: A strategy to improve the accuracy of short-term wind power prediction under wind speed offset scenarios," Applied Energy, Elsevier, vol. 377(PD).
  8. Zeynep Ceylan, 2020. "Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 944-956, September.
  9. Yang, Mao & Guo, Yunfeng & Fan, Fulin & Huang, Tao, 2024. "Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering," Energy, Elsevier, vol. 302(C).
  10. Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna, 2023. "Physics-informed Gaussian process regression for states estimation and forecasting in power grids," International Journal of Forecasting, Elsevier, vol. 39(2), pages 967-980.
  11. Zhu, Yingqin & Liu, Yue & Wang, Nan & Zhang, ZhaoZhao & Li, YuanQiang, 2025. "Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction," Applied Energy, Elsevier, vol. 379(C).
  12. Yanghe Liu & Hairong Zhang & Chuanfeng Wu & Mengxin Shao & Liting Zhou & Wenlong Fu, 2024. "A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Improved Northern Goshawk Optimization for Numerical," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
  13. 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.
  14. Yang, Mao & Guo, Yunfeng & Huang, Yutong, 2023. "Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process," Energy, Elsevier, vol. 282(C).
  15. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
  16. 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.
  17. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
  18. Mohammed Chakib Sekkal & Zakarya Ziani & Moustafa Yassine Mahdad & Sidi Mohammed Meliani & Mohammed Haris Baghli & Mohammed Zakaria Bessenouci, 2024. "Assessing the Wind Power Potential in Naama, Algeria to Complement Solar Energy through Integrated Modeling of the Wind Resource and Turbine Wind Performance," Energies, MDPI, vol. 17(4), pages 1-34, February.
  19. Xiong, Xiong & Zou, Ruilin & Sheng, Tao & Zeng, Weilin & Ye, Xiaoling, 2023. "An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed," Energy, Elsevier, vol. 283(C).
  20. Zhang, Zeguo & Yin, Jianchuan, 2025. "Incremental principal component analysis based depthwise separable Unet model for complex wind system forecasting," Energy, Elsevier, vol. 334(C).
  21. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
  22. Yang, Jian & Wang, Li & Song, Dongran & Huang, Chaoneng & Huang, Liansheng & Wang, Junlei, 2022. "Incorporating environmental impacts into zero-point shifting diagnosis of wind turbines yaw angle," Energy, Elsevier, vol. 238(PA).
  23. 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).
  24. Duan, Zhu & Liu, Hui & Li, Ye & Nikitas, Nikolaos, 2022. "Time-variant post-processing method for long-term numerical wind speed forecasts based on multi-region recurrent graph network," Energy, Elsevier, vol. 259(C).
  25. Zhu, Enping & Li, Tao & Xiong, Jinbiao & Chai, Xiang & Zhang, Tengfei & Liu, Xiaojing, 2026. "A digital twin framework for real-time operation monitoring and its future prediction for space nuclear power," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).
  26. Bingzi Jin & Xiaojie Xu, 2025. "Forecasts of coking coal futures price indices through Gaussian process regressions," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 203-217, March.
  27. Nadire Cavus & Yakubu Bala Mohammed & Abdulsalam Ya’u Gital & Mohammed Bulama & Adamu Muhammad Tukur & Danlami Mohammed & Muhammad Lamir Isah & Abba Hassan, 2022. "Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
  28. 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.
  29. Paulino José García-Nieto & Esperanza García-Gonzalo & José Ramón Alonso Fernández & Cristina Díaz Muñiz, 2020. "A New Predictive Model for Evaluating Chlorophyll-a Concentration in Tanes Reservoir by Using a Gaussian Process Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4921-4941, December.
  30. Antonio C. C. Perrelli & Eduardo A. Sodré & André V. R. N. Silva & Caarem D. S. Studzinski & Vinícius F. Silva & Dalton F. G. Filho & Armando T. Neto & Alex A. B. Santos, 2024. "Optimizing Price Markup: The Impact of Power Purchase Agreements and Energy Production Uncertainty on the Economic Performance of Onshore and Offshore Wind Farms," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 211-219, September.
  31. Paweł Piotrowski & Marcin Kopyt & Dariusz Baczyński & Sylwester Robak & Tomasz Gulczyński, 2021. "Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine," Energies, MDPI, vol. 14(5), pages 1-25, February.
  32. Fuhao Chen & Linyue Gao, 2025. "Learning Residual Distributions with Diffusion Models for Probabilistic Wind Power Forecasting," Energies, MDPI, vol. 18(16), pages 1-19, August.
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