OpenAlex Citation Counts

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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:

Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN
Xiaoxun Zhu, Ruizhang Liu, Yao Chen, et al.
Energy (2021) Vol. 236, pp. 121523-121523
Closed Access | Times Cited: 58

Showing 1-25 of 58 citing articles:

Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
Zeni Zhao, Sining Yun, Lingyun Jia, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 105982-105982
Closed Access | Times Cited: 163

Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model
Dongxiao Niu, Lijie Sun, Min Yu, et al.
Energy (2022) Vol. 254, pp. 124384-124384
Closed Access | Times Cited: 109

A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network
Ziyuan Zhang, Jianzhou Wang, Danxiang Wei, et al.
Renewable Energy (2023) Vol. 204, pp. 11-23
Closed Access | Times Cited: 62

Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN
Qingyang Li, Guosong Wang, Xinrong Wu, et al.
Energy (2024) Vol. 299, pp. 131448-131448
Closed Access | Times Cited: 22

A novel frequency-domain physics-informed neural network for accurate prediction of 3D Spatio-temporal wind fields in wind turbine applications
Shaopeng Li, Xin Li, Yan Jiang, et al.
Applied Energy (2025) Vol. 386, pp. 125526-125526
Closed Access | Times Cited: 5

Sustainable energies and machine learning: An organized review of recent applications and challenges
Pouya Ifaei, Morteza Nazari‐Heris, Amir Saman Tayerani Charmchi, et al.
Energy (2022) Vol. 266, pp. 126432-126432
Closed Access | Times Cited: 49

Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory
Shenghui Zhang, Chen Wang, Peng Liao, et al.
Expert Systems with Applications (2022) Vol. 193, pp. 116509-116509
Closed Access | Times Cited: 45

A short-term wind speed interval prediction method based on WRF simulation and multivariate line regression for deep learning algorithms
Yan Han, Lihua Mi, Lian Shen, et al.
Energy Conversion and Management (2022) Vol. 258, pp. 115540-115540
Closed Access | Times Cited: 40

Hybrid Inception-embedded deep neural network ResNet for short and medium-term PV-Wind forecasting
Adeel Feroz Mirza, Majad Mansoor, Muhammad Usman, et al.
Energy Conversion and Management (2023) Vol. 294, pp. 117574-117574
Closed Access | Times Cited: 34

Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features
Gang Li, Chen Lin, Yupeng Li
Energies (2025) Vol. 18, Iss. 3, pp. 652-652
Open Access | Times Cited: 1

A location-centric transformer framework for multi-location short-term wind speed forecasting
Luyang Zhao, Changliang Liu, Chaojie Yang, et al.
Energy Conversion and Management (2025) Vol. 328, pp. 119627-119627
Closed Access | Times Cited: 1

A review of ultra-short-term forecasting of wind power based on data decomposition-forecasting technology combination model
Yulong Chen, Xue Hu, Lixin Zhang
Energy Reports (2022) Vol. 8, pp. 14200-14219
Open Access | Times Cited: 31

A hybrid methodology using VMD and disentangled features for wind speed forecasting
Srihari Parri, Kiran Teeparthi, Vishalteja Kosana
Energy (2023) Vol. 288, pp. 129824-129824
Closed Access | Times Cited: 21

Novel wind-speed prediction system based on dimensionality reduction and nonlinear weighting strategy for point-interval prediction
Xinyu Wang, Jianzhou Wang, Xinsong Niu, et al.
Expert Systems with Applications (2023) Vol. 241, pp. 122477-122477
Closed Access | Times Cited: 17

An efficient wind speed prediction method based on a deep neural network without future information leakage
Ke Li, Ruifang Shen, Zhenguo Wang, et al.
Energy (2022) Vol. 267, pp. 126589-126589
Closed Access | Times Cited: 23

Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations
Bowen Yan, Ruifang Shen, Ke Li, et al.
Energy (2023) Vol. 284, pp. 128418-128418
Closed Access | Times Cited: 14

VMD-SCINet: a hybrid model for improved wind speed forecasting
Srihari Parri, Kiran Teeparthi
Earth Science Informatics (2023) Vol. 17, Iss. 1, pp. 329-350
Closed Access | Times Cited: 12

Effects of spatiotemporal correlations in wind data on neural network-based wind predictions
Heesoo Shin, Mario Rüttgers, Sang-Seung Lee
Energy (2023) Vol. 279, pp. 128068-128068
Open Access | Times Cited: 11

Parallel Multiple CNNs With Temporal Predictions for Wind Turbine Blade Cracking Early Fault Detection
Quan Lu, Wanxing Ye, Linfei Yin
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-11
Closed Access | Times Cited: 4

Ultra-short-term wind power prediction model based on fixed scale dual mode decomposition and deep learning networks
Jiuyuan Huo, Jihao Xu, Chen Chang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108501-108501
Closed Access | Times Cited: 4

CMLLM: A novel cross-modal large language model for wind power forecasting
Guopeng Zhu, Weiqing Jia, Zhitai Xing, et al.
Energy Conversion and Management (2025) Vol. 330, pp. 119673-119673
Closed Access

Ultra-short-term wind speed forecasting based on secondary decomposition and Transformer-MLR combined model
Yong Yue, Weiming Zheng, Anguo Wu, et al.
Electric Power Systems Research (2025) Vol. 246, pp. 111702-111702
Closed Access

A coupling deterministic and probabilistic wind energy prediction based on information leakage prevention and distinctive deep learning network
Jujie Wang, Yafen Liu, Shuqin Shu, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 152, pp. 110862-110862
Closed Access

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