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A hybrid system for short-term wind speed forecasting

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Cited by:

  1. Sörries, Bernd & Stronzik, Marcus & Tenbrock, Sebastian & Wernick, Christian & Wissner, Matthias, 2019. "Die ökonomische Relevanz und Entwicklungsperspektiven von Blockchain: Analysen für den Telekommunikations- und Energiemarkt," WIK Discussion Papers 445, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH.
  2. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
  3. Mesbaholdin Salami & Farzad Movahedi Sobhani & Mohammad Sadegh Ghazizadeh, 2018. "Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market," Data, MDPI, vol. 3(4), pages 1-26, October.
  4. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
  5. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
  6. Liu, Hui & Chen, Chao, 2019. "Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction," Applied Energy, Elsevier, vol. 254(C).
  7. Lei Zhang & Lun Xie & Qinkai Han & Zhiliang Wang & Chen Huang, 2020. "Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation," Energies, MDPI, vol. 13(22), pages 1-24, November.
  8. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
  9. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
  10. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
  11. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
  12. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  13. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
  14. Deng, Wanru & Liu, Liqin & Dai, Yuanjun & Wu, Haitao & Yuan, Zhiming, 2024. "A prediction method for blade deformations of large-scale FVAWTs using dynamics theory and machine learning techniques," Energy, Elsevier, vol. 304(C).
  15. Wei, Danxiang & Wang, Jianzhou & Niu, Xinsong & Li, Zhiwu, 2021. "Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks," Applied Energy, Elsevier, vol. 292(C).
  16. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
  17. Tingqi Wang & Yuting Guo & Mazina Svetlana Evgenievna & Zhenjiang Wu, 2024. "Application of a Multi-Model Fusion Forecasting Approach in Runoff Prediction: A Case Study of the Yangtze River Source Region," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
  18. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
  19. 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.
  20. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
  21. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
  22. 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.
  23. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
  24. Qinkai Han & Hao Wu & Tao Hu & Fulei Chu, 2018. "Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models," Energies, MDPI, vol. 11(11), pages 1-23, November.
  25. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
  26. Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
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