IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v138y2017icp977-990.html
   My bibliography  Save this item

A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Long, Huan & Zhang, Zijun & Sun, Mu-Xia & Li, Yan-Fu, 2018. "The data-driven schedule of wind farm power generations and required reserves," Energy, Elsevier, vol. 149(C), pages 485-495.
  2. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
  3. David Barbosa de Alencar & Carolina De Mattos Affonso & Roberto Célio Limão de Oliveira & Jorge Laureano Moya Rodríguez & Jandecy Cabral Leite & José Carlos Reston Filho, 2017. "Different Models for Forecasting Wind Power Generation: Case Study," Energies, MDPI, vol. 10(12), pages 1-27, November.
  4. Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
  5. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
  6. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
  7. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
  8. Hua Li & Zhen Wang & Binbin Shan & Lingling Li, 2022. "Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction," Energies, MDPI, vol. 15(22), pages 1-21, November.
  9. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
  10. 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).
  11. Qu, Zhijian & Li, Jian & Hou, Xinxing & Gui, Jianglin, 2023. "A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction," Energy, Elsevier, vol. 281(C).
  12. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
  13. 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.
  14. Tang, Ling & Wang, Haohan & Li, Ling & Yang, Kaitong & Mi, Zhifu, 2020. "Quantitative models in emission trading system research: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  15. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
  16. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
  17. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
  18. Lu, Peng & Ye, Lin & Pei, Ming & Zhao, Yongning & Dai, Binhua & Li, Zhuo, 2022. "Short-term wind power forecasting based on meteorological feature extraction and optimization strategy," Renewable Energy, Elsevier, vol. 184(C), pages 642-661.
  19. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
  20. Shuang Lei & Yu He & Jing Zhang & Kun Deng, 2023. "Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition," Energies, MDPI, vol. 16(11), pages 1-19, May.
  21. Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).
  22. Qin, Li & Liu, Shi & Kang, Yi & Yan, Song An & Inaki Schlaberg, H. & Wang, Zhan, 2019. "Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement," Energy, Elsevier, vol. 167(C), pages 1236-1250.
  23. Jiang, Ping & Wang, Biao & Li, Hongmin & Lu, Haiyan, 2019. "Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting," Energy, Elsevier, vol. 173(C), pages 468-482.
  24. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
  25. Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
  26. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
  27. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
  28. 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).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.