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Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction

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Cited by:

  1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
  2. Henrik Zsiborács & Gábor Pintér & András Vincze & Nóra Hegedűsné Baranyai, 2022. "Wind Power Generation Scheduling Accuracy in Europe: An Overview of ENTSO-E Countries," Sustainability, MDPI, vol. 14(24), pages 1-58, December.
  3. Wenlong Fu & Kai Wang & Jianzhong Zhou & Yanhe Xu & Jiawen Tan & Tie Chen, 2019. "A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
  4. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
  5. 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.
  6. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Mohamed Abd Elaziz & Ahmed H. Samak, 2022. "Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer," Energies, MDPI, vol. 15(24), pages 1-14, December.
  7. Hu, Jianming & Heng, Jiani & Wen, Jiemei & Zhao, Weigang, 2020. "Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm," Renewable Energy, Elsevier, vol. 162(C), pages 1208-1226.
  8. Gong, Zhipeng & Wan, Anping & Ji, Yunsong & AL-Bukhaiti, Khalil & Yao, Zhehe, 2024. "Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model," Energy, Elsevier, vol. 295(C).
  9. Sizhou Sun & Lisheng Wei & Jie Xu & Zhenni Jin, 2019. "A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN," Energies, MDPI, vol. 12(3), pages 1-24, January.
  10. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
  11. Yuqing Yang & Stephen Bremner & Chris Menictas & Merlinde Kay, 2019. "A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques," Energies, MDPI, vol. 12(12), pages 1-25, June.
  12. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "A hybrid VMD based contextual feature representation approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 219(P1).
  13. Deyun Wang & Yanling Liu & Zeng Wu & Hongxue Fu & Yong Shi & Haixiang Guo, 2018. "Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
  14. Hu, Shuai & Xiang, Yue & Zhang, Hongcai & Xie, Shanyi & Li, Jianhua & Gu, Chenghong & Sun, Wei & Liu, Junyong, 2021. "Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction," Applied Energy, Elsevier, vol. 293(C).
  15. 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).
  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. Biao Xiong & Ruiping Li & Dong Ren & Huigang Liu & Tao Xu & Yingping Huang, 2021. "Prediction of flooding in the downstream of the Three Gorges Reservoir based on a back propagation neural network optimized using the AdaBoost algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1559-1575, June.
  18. Chen, Linfei & Zhao, Xuefeng, 2024. "A multiscale and multivariable differentiated learning for carbon price forecasting," Energy Economics, Elsevier, vol. 131(C).
  19. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
  20. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
  21. Yu, Chuanjin & Li, Yongle & Chen, Qian & Lai, Xiaopan & Zhao, Liyang, 2022. "Matrix-based wavelet transformation embedded in recurrent neural networks for wind speed prediction," Applied Energy, Elsevier, vol. 324(C).
  22. Khodayar, Mahdi & Saffari, Mohsen & Williams, Michael & Jalali, Seyed Mohammad Jafar, 2022. "Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting," Energy, Elsevier, vol. 254(PB).
  23. Feng, Cong & Sun, Mucun & Cui, Mingjian & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "Characterizing forecastability of wind sites in the United States," Renewable Energy, Elsevier, vol. 133(C), pages 1352-1365.
  24. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
  25. 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).
  26. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
  27. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
  28. Li, Yanfei & Shi, Huipeng & Han, Fengze & Duan, Zhu & Liu, Hui, 2019. "Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy," Renewable Energy, Elsevier, vol. 135(C), pages 540-553.
  29. He, Kaijian & Tso, Geoffrey K.F. & Zou, Yingchao & Liu, Jia, 2018. "Crude oil risk forecasting: New evidence from multiscale analysis approach," Energy Economics, Elsevier, vol. 76(C), pages 574-583.
  30. Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
  31. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
  32. 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.
  33. Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
  34. Wu, Zhou & Zeng, Shaoxiong & Jiang, Ruiqi & Zhang, Haoran & Yang, Zhile, 2023. "Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks," Energy, Elsevier, vol. 270(C).
  35. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
  36. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
  37. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
  38. 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.
  39. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
  40. Wang, Deyun & Yue, Chenqiang & ElAmraoui, Adnen, 2021. "Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
  41. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
  42. Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
  43. Xu, Bin & Lin, Boqiang, 2022. "Exploring the spatial distribution of distributed energy in China," Energy Economics, Elsevier, vol. 107(C).
  44. Ali Jallal, Mohammed & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A novel deep neural network based on randomly occurring distributed delayed PSO algorithm for monitoring the energy produced by four dual-axis solar trackers," Renewable Energy, Elsevier, vol. 149(C), pages 1182-1196.
  45. 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.
  46. Lin, Boqiang & Zhang, Chongchong, 2021. "A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China," Renewable Energy, Elsevier, vol. 179(C), pages 1565-1577.
  47. Xiaohan Huang & Aihua Jiang, 2022. "Wind Power Generation Forecast Based on Multi-Step Informer Network," Energies, MDPI, vol. 15(18), pages 1-17, September.
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