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Short-term wind speed prediction model based on GA-ANN improved by VMD

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Cited by:

  1. Li, Tian & Ai, Lijuan & Yang, Qingshan & Zhang, Xingxin & Li, Hang & Lu, Dawei & Shen, Hongtao, 2025. "Short-term wind power prediction based on multiscale numerical simulation coupled with deep learning," Renewable Energy, Elsevier, vol. 246(C).
  2. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).
  3. Jin, Ji & Tian, Jinyu & Yu, Min & Wu, Yong & Tang, Yuanyan, 2024. "A novel ultra-short-term wind speed prediction method based on dynamic adaptive continued fraction," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
  4. Li, Shibao & Guo, Liang & Zhu, Jinze & Liu, Menglong & Chen, Jiaxin & Meng, Zihan, 2026. "Short-term multi-step wind speed forecasting with multi-feature inputs using Variational Mode Decomposition, a novel artificial intelligence network, and the Polar Lights Optimizer," Renewable Energy, Elsevier, vol. 256(PB).
  5. Yang Liu & Li Hu Wang & Li Bo Yang & Xue Mei Liu, 2022. "Drought prediction based on an improved VMD-OS-QR-ELM model," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-13, January.
  6. Zhu, Ting & Wang, Wenbo & Yu, Min, 2022. "A novel blood glucose time series prediction framework based on a novel signal decomposition method," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
  7. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
  8. Guanjun Liu & Chao Wang & Hui Qin & Jialong Fu & Qin Shen, 2022. "A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting," Energies, MDPI, vol. 15(19), pages 1-16, September.
  9. Chen, Zhengganzhe & Zhang, Bin & Du, Chenglong & Meng, Wei & Meng, Anbo, 2024. "A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction," Energy, Elsevier, vol. 294(C).
  10. Sun, Yang & Tian, Zhirui, 2025. "Solving few-shot problem in wind speed prediction: A novel transfer strategy based on decomposition and learning ensemble," Applied Energy, Elsevier, vol. 377(PD).
  11. Qingyuan Wang & Longnv Huang & Jiehui Huang & Qiaoan Liu & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
  12. Xu, Yuzhen & Huang, Xin & Zheng, Xidong & Zeng, Ziyang & Jin, Tao, 2024. "VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy," Renewable Energy, Elsevier, vol. 236(C).
  13. 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).
  14. 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).
  15. Yunhe Cheng & Beibei Hu, 2022. "Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine," Energies, MDPI, vol. 15(10), pages 1-18, May.
  16. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
  17. Xi Zhang & Longyun Kang & Xuemei Wang & Yangbo Liu & Sheng Huang, 2025. "Capacity Optimization Configuration of Hybrid Energy Storage Systems for Wind Farms Based on Improved k-means and Two-Stage Decomposition," Energies, MDPI, vol. 18(4), pages 1-24, February.
  18. Li, Mingjun & Zhang, Kequan & Kou, Menggang & Ma, Yining, 2025. "An offshore wind speed forecasting system based on feature enhancement, deep time series clustering, and extended LSTM," Energy, Elsevier, vol. 333(C).
  19. Hexiang Zheng & Hongfei Hou & Ziyuan Qin, 2024. "Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves," Sustainability, MDPI, vol. 16(21), pages 1-18, October.
  20. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
  21. 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).
  22. Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
  23. 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).
  24. Zhong, Mingwei & Fan, Jingmin & Luo, Jianqiang & Xiao, Xuanyi & He, Guanglin & Cai, Rui, 2024. "InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation," Applied Energy, Elsevier, vol. 371(C).
  25. 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.
  26. Dai, Huning & Wang, Ziqian & Xia, Mingjie & Cheung, Yuyam & Guo, Zhiming & Gong, Cheng, 2026. "Intelligent forecasting and techno-economic optimization of a grid-connected hybrid solar-wind system: A case study of Longgang, China," Renewable Energy, Elsevier, vol. 256(PI).
  27. Penghui Li & Ke Li & Fang Wang & Zonglong Zhang & Shuang Cai & Liang Cheng, 2022. "A Novel Method for Gas Disaster Prevention during the Construction Period in Coal Penetration Tunnels—A Stepwise Prediction of Gas Concentration Based on the LSTM Method," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
  28. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
  29. 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).
  30. Verdone, Alessio & Panella, Massimo & De Santis, Enrico & Rizzi, Antonello, 2025. "A review of solar and wind energy forecasting: From single-site to multi-site paradigm," Applied Energy, Elsevier, vol. 392(C).
  31. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
  32. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
  33. Maria Ashraf & Bushra Raza & Maryam Arshad & Bilal Muhammad Khan & Syed Sajjad Haider Zaidi, 2024. "Performance enhancement of short-term wind speed forecasting model using Realtime data," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-19, May.
  34. Wu, Jie & Jin, Yuhao & Luo, Wenjun, 2025. "Prior and synergistic effects in multi-source information fusion for optimal wind speed forecasting model selection," Energy, Elsevier, vol. 337(C).
  35. Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  36. 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.
  37. Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
  38. 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).
  39. Laeeq Aslam & Runmin Zou & Ebrahim Shahzad Awan & Sayyed Shahid Hussain & Kashish Ara Shakil & Mudasir Ahmad Wani & Muhammad Asim, 2025. "Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices," Energies, MDPI, vol. 18(9), pages 1-21, April.
  40. 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.
  41. Wang, Zhimin & Kong, Fance & Chan, Wenwu & Yue, Shangyu, 2025. "Study on the effect of wind direction on the thermal performance of cavity receiver for trough solar system: Artificial neural network approach based on genetic algorithm," Energy, Elsevier, vol. 320(C).
  42. Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
  43. 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).
  44. Li, Qingyang & Wang, Guosong & Wu, Xinrong & Gao, Zhigang & Dan, Bo, 2024. "Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN," Energy, Elsevier, vol. 299(C).
  45. Liu, Chien-Liang & Chang, Tzu-Yu & Yang, Jie-Si & Huang, Kai-Bin, 2023. "A deep learning sequence model based on self-attention and convolution for wind power prediction," Renewable Energy, Elsevier, vol. 219(P1).
  46. Leng, Zhiyuan & Chen, Lu & Yi, Bin & Liu, Fanqian & Xie, Tao & Mei, Ziyi, 2025. "Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition," Energy, Elsevier, vol. 322(C).
  47. 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).
  48. Shijun Wang & Chun Liu & Kui Liang & Ziyun Cheng & Xue Kong & Shuang Gao, 2022. "Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
  49. Yang, Yusong & Solomin, Evgeny V., 2025. "Wind direction prediction based on nonlinear autoregression and Elman neural networks for the wind turbine yaw system," Renewable Energy, Elsevier, vol. 241(C).
  50. 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).
  51. Fu, Zhengze & Qian, Hongliang & Chu, Xuanxuan & Yang, Fan & Guo, Chengchao & Wang, Fuming, 2025. "Hybrid ultra-short-term wind speed forecasting model based on improved BO-HMA-BiGRU and GMBInformer: Integrating SVMD-SWT dual decomposition and MPE-driven modeling mechanism," Energy, Elsevier, vol. 339(C).
  52. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
  53. Yang, Zihao & Dong, Sheng, 2023. "A novel decomposition-based approach for non-stationary hub-height wind speed modelling," Energy, Elsevier, vol. 283(C).
  54. Honghui Liu & Donghui Li & Zhong Xiao & Qiansheng Qiu & Xinjie Tao & Qifeng Qian & Mengxin Jiang & Wei Yu, 2025. "Power Allocation and Capacity Optimization Configuration of Hybrid Energy Storage Systems in Microgrids Using RW-GWO-VMD," Energies, MDPI, vol. 18(16), pages 1-25, August.
  55. Yilmaz, Ceyhun & Koyuncu, Ismail, 2021. "Thermoeconomic modeling and artificial neural network optimization of Afyon geothermal power plant," Renewable Energy, Elsevier, vol. 163(C), pages 1166-1181.
  56. Yin, Wanjun & Ji, Jianbo, 2025. "Prediction of EV charging load based on federated learning," Energy, Elsevier, vol. 316(C).
  57. He, Jinhua & Hu, Zechun & Wang, Songpo & Mujeeb, Asad & Yang, Pengwei, 2024. "Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention," Energy, Elsevier, vol. 310(C).
  58. Zhou, Jianguo & Zhou, Luming & Zhao, Yunlong & Wu, Kai, 2025. "Significant wave height prediction based on improved fuzzy C-means clustering and bivariate kernel density estimation," Renewable Energy, Elsevier, vol. 245(C).
  59. Zheng, Jingwei & Wang, Jianzhou, 2024. "Short-term wind speed forecasting based on recurrent neural networks and Levy crystal structure algorithm," Energy, Elsevier, vol. 293(C).
  60. Xiao, Wenjing & Mo, Li & Xu, Zhanxing & Liu, Chang & Zhang, Yongchuan, 2024. "A hybrid electric load forecasting model based on decomposition considering fisher information," Applied Energy, Elsevier, vol. 364(C).
  61. Zhu, Anfeng & Zhao, Qiancheng & Shi, Zhaoyao & Yang, Tianlong & Zhou, Ling & Zeng, Bing, 2024. "A novel combined model based on advanced optimization algorithm, and deep learning model for abnormal wind speed identification and reconstruction," Energy, Elsevier, vol. 312(C).
  62. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
  63. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
  64. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective of China," Energy, Elsevier, vol. 283(C).
  65. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
  66. Zhang, Chu & Ji, Chunlei & Hua, Lei & Ma, Huixin & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction," Renewable Energy, Elsevier, vol. 197(C), pages 668-682.
  67. Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.
  68. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
  69. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
  70. Shi, Jian & Teh, Jiashen, 2024. "Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion," Applied Energy, Elsevier, vol. 353(PB).
  71. Li, Yanhui & Sun, Kaixuan & Yao, Qi & Wang, Lin, 2024. "A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm," Energy, Elsevier, vol. 286(C).
  72. Che, Jinxing & Yuan, Fang & Deng, Dewen & Jiang, Zheyong, 2023. "Ultra-short-term probabilistic wind power forecasting with spatial-temporal multi-scale features and K-FSDW based weight," Applied Energy, Elsevier, vol. 331(C).
  73. Xiao, Yiping & Wei, Honghao & Shi, Ying & Zhang, Haiyang & Shen, Zongtao & Jiao, Hongjian, 2025. "A short-term wind power prediction based on MCOOT optimized deep learning networks and attention-weighted environmental factors for error correction," Energy, Elsevier, vol. 324(C).
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