Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Multi‐step wind power forecast based on VMD‐LSTM
Li Han, Rongchang Zhang, Xuesong Wang, et al.
IET Renewable Power Generation (2019) Vol. 13, Iss. 10, pp. 1690-1700
Closed Access | Times Cited: 137

Showing 1-25 of 137 citing articles:

A review of wind speed and wind power forecasting with deep neural networks
Yun Wang, Runmin Zou, Fang Liu, et al.
Applied Energy (2021) Vol. 304, pp. 117766-117766
Closed Access | Times Cited: 538
An improved residual-based convolutional neural network for very short-term wind power forecasting
Ceyhun Yıldız, Hakan Açıkgöz, Deniz Korkmaz, et al.
Energy Conversion and Management (2020) Vol. 228, pp. 113731-113731
Closed Access | Times Cited: 192
Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network
Jiandong Duan, Peng Wang, Wentao Ma, et al.
Energy (2020) Vol. 214, pp. 118980-118980
Closed Access | Times Cited: 175
A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction
Jinlin Xiong, Peng Tian, Zihan Tao, et al.
Energy (2022) Vol. 266, pp. 126419-126419
Closed Access | Times Cited: 144
Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction
Haipeng Chen, Hao Wu, Tianyang Kan, et al.
International Journal of Electrical Power & Energy Systems (2023) Vol. 154, pp. 109420-109420
Open Access | Times Cited: 127
A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting
Jiandong Duan, Peng Wang, Wentao Ma, et al.
International Journal of Electrical Power & Energy Systems (2021) Vol. 134, pp. 107452-107452
Closed Access | Times Cited: 110
A hybrid deep learning technology for PM2.5 air quality forecasting
Zhendong Zhang, Yongkang Zeng, Ke Yan
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 29, pp. 39409-39422
Closed Access | Times Cited: 108
A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error
Jikai Duan, Mingheng Chang, Xiangyue Chen, et al.
Renewable Energy (2022) Vol. 200, pp. 788-808
Closed Access | Times Cited: 79
Wind power prediction based on EEMD-Tent-SSA-LS-SVM
Zheng Li, Xiaorui Luo, Mengjie Liu, et al.
Energy Reports (2022) Vol. 8, pp. 3234-3243
Open Access | Times Cited: 76
A multi-step wind power group forecasting seq2seq architecture with spatial–temporal feature fusion and numerical weather prediction correction
Shiwei Xu, Yongjun Wang, Xinglei Xu, et al.
Energy (2024) Vol. 291, pp. 130352-130352
Closed Access | Times Cited: 14
A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges
Zongxu Liu, Hui Guo, Y. Zhang, et al.
Energies (2025) Vol. 18, Iss. 2, pp. 350-350
Open Access | Times Cited: 1
Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation
Bowen Zhou, Xiangjin Ma, Yanhong Luo, et al.
IEEE Access (2019) Vol. 7, pp. 165279-165292
Open Access | Times Cited: 142
A new crude oil price forecasting model based on variational mode decomposition
Yusheng Huang, Yong Deng
Knowledge-Based Systems (2020) Vol. 213, pp. 106669-106669
Closed Access | Times Cited: 129
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review
Santhosh Madasthu, Chintham Venkaiah, D. M. Vinod Kumar
Engineering Reports (2020) Vol. 2, Iss. 6
Open Access | Times Cited: 119
Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms
Amirhossein Ahmadi, Mojtaba Nabipour, Behnam Mohammadi‐Ivatloo, et al.
IEEE Access (2020) Vol. 8, pp. 151511-151522
Open Access | Times Cited: 104
A novel decomposition-ensemble prediction model for ultra-short-term wind speed
Zhongda Tian, Hao Chen
Energy Conversion and Management (2021) Vol. 248, pp. 114775-114775
Closed Access | Times Cited: 101
A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture
Hao Yin, Zuhong Ou, Jiajin Fu, et al.
Energy (2021) Vol. 234, pp. 121271-121271
Closed Access | Times Cited: 79
Modes decomposition forecasting approach for ultra-short-term wind speed
Zhongda Tian
Applied Soft Computing (2021) Vol. 105, pp. 107303-107303
Closed Access | Times Cited: 77
An advanced short-term wind power forecasting framework based on the optimized deep neural network models
Seyed Mohammad Jafar Jalali, Sajad Ahmadian, Mahdi Khodayar, et al.
International Journal of Electrical Power & Energy Systems (2022) Vol. 141, pp. 108143-108143
Closed Access | Times Cited: 57
Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs
M. Zou, Ninoslav Holjevac, Josip Đaković, et al.
IEEE Transactions on Sustainable Energy (2022) Vol. 13, Iss. 2, pp. 1169-1187
Closed Access | Times Cited: 46
An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting
Xiaoran Dai, Shuai Liu, Wenshan Hu
Energy (2023) Vol. 272, pp. 127173-127173
Closed Access | Times Cited: 38
Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research
Han Peng, Songyin Li, Linjian Shangguan, et al.
Sustainability (2023) Vol. 15, Iss. 10, pp. 8333-8333
Open Access | Times Cited: 26
A short-term wind power prediction approach based on an improved dung beetle optimizer algorithm, variational modal decomposition, and deep learning
Yan He, Wei Wang, Meng Li, et al.
Computers & Electrical Engineering (2024) Vol. 116, pp. 109182-109182
Closed Access | Times Cited: 11
A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model
Bingchun Liu, Shijie Zhao, Xiaogang Yu, et al.
Energies (2020) Vol. 13, Iss. 18, pp. 4964-4964
Open Access | Times Cited: 63
Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior
Xiaorui Shao, Chang Soo Kim
IEEE Access (2020) Vol. 8, pp. 125263-125273
Open Access | Times Cited: 57
Page 1 - Next Page

Scroll to top