Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting
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DOI: 10.1016/j.renene.2023.05.004
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- Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren & Liu, Jizhen, 2017. "Measurement and statistical analysis of wind speed intermittency," Energy, Elsevier, vol. 118(C), pages 632-643.
- Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.
- Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
- Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
- Dong, Lei & Wang, Lijie & Khahro, Shahnawaz Farhan & Gao, Shuang & Liao, Xiaozhong, 2016. "Wind power day-ahead prediction with cluster analysis of NWP," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1206-1212.
- Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
- Pearre, Nathaniel & Adye, Katherine & Swan, Lukas, 2019. "Proportioning wind, solar, and in-stream tidal electricity generating capacity to co-optimize multiple grid integration metrics," Applied Energy, Elsevier, vol. 242(C), pages 69-77.
- Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
- Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
- Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
- Scholz, Teresa & Lopes, Vitor V. & Estanqueiro, Ana, 2014. "A cyclic time-dependent Markov process to model daily patterns in wind turbine power production," Energy, Elsevier, vol. 67(C), pages 557-568.
- Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
- Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
- Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
- Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
- 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).
- Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
- Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
- Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
- Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
- Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
- Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
- Zuluaga, Carlos D. & Álvarez, Mauricio A. & Giraldo, Eduardo, 2015. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Elsevier, vol. 156(C), pages 321-330.
- Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
- Liao, Chiung-Chou, 2010. "Genetic k-means algorithm based RBF network for photovoltaic MPP prediction," Energy, Elsevier, vol. 35(2), pages 529-536.
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- Yifei Deng & Yijing Wang & Xiaofan Xing & Yuankang Xiong & Siqing Xu & Rong Wang, 2024. "Requirement on the Capacity of Energy Storage to Meet the 2 °C Goal," Sustainability, MDPI, vol. 16(9), pages 1-17, April.
- 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).
- Xiong, Zhanhang & Yao, Jianjiang & Huang, Yongmin & Yu, Zhaoxu & Liu, Yalei, 2024. "A wind speed forecasting method based on EMD-MGM with switching QR loss function and novel subsequence superposition," Applied Energy, Elsevier, vol. 353(PB).
- 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).
- Yang, Mao & Huang, Yutong & Guo, Yunfeng & Zhang, Wei & Wang, Bo, 2024. "Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism," Energy, Elsevier, vol. 290(C).
- Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
- Yang, Mao & Wang, Da & Zhang, Wei, 2024. "A novel ultra short-term wind power prediction model based on double model coordination switching mechanism," Energy, Elsevier, vol. 289(C).
- 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).
- 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).
- Jia Lu & Jiaqi Zhao & Zheng Zhang & Yaxin Liu & Yang Xu & Tao Wang & Yuqi Yang, 2024. "Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty," Energies, MDPI, vol. 17(20), pages 1-18, October.
- Mingyi Liu & Bin Zhang & Jiaqi Wang & Han Liu & Jianxing Wang & Chenghao Liu & Jiahui Zhao & Yue Sun & Rongrong Zhai & Yong Zhu, 2023. "Optimal Configuration of Wind-PV and Energy Storage in Large Clean Energy Bases," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
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Keywords
Wind speed; Power transfer; Fluctuation partition; Wind power forecasting;All these keywords.
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