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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:

Deep learning approach for wind speed forecasts at turbine locations in a wind farm
Peng Kou, Chen Wang, Deliang Liang, et al.
IET Renewable Power Generation (2020) Vol. 14, Iss. 13, pp. 2416-2428
Open Access | Times Cited: 15

Showing 15 citing articles:

A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm
Xuefang Xu, Shiting Hu, Huaishuang Shao, et al.
Energy (2023) Vol. 284, pp. 128565-128565
Closed Access | Times Cited: 48
Multi-Source and Temporal Attention Network for Probabilistic Wind Power Prediction
Hao Zhang, Jie Yan, Yongqian Liu, et al.
IEEE Transactions on Sustainable Energy (2021) Vol. 12, Iss. 4, pp. 2205-2218
Closed Access | Times Cited: 82
Augmented Convolutional Network for Wind Power Prediction: A New Recurrent Architecture Design With Spatial-Temporal Image Inputs
Lilin Cheng, Haixiang Zang, Yan Xu, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 17, Iss. 10, pp. 6981-6993
Closed Access | Times Cited: 42
Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion
Yu Huang, Bingzhe Zhang, Huizhen Pang, et al.
Renewable Energy (2022) Vol. 192, pp. 526-536
Closed Access | Times Cited: 32
A novel hybrid algorithm based on Empirical Fourier decomposition and deep learning for wind speed forecasting
Bhupendra Kumar, Neha Yadav, Sunil Kumar
Energy Conversion and Management (2023) Vol. 300, pp. 117891-117891
Closed Access | Times Cited: 16
An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980–2017
Mekyung Lee, Gang He
Journal of Cleaner Production (2021) Vol. 297, pp. 126536-126536
Closed Access | Times Cited: 36
A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines
Qian Li, Danyang Chen, Lin Huang, et al.
Energies (2025) Vol. 18, Iss. 3, pp. 588-588
Open Access
Overview of Data-Driven Models for Wind Turbine Wake Flows
Maokun Ye, Min Li, Mingqiu Liu, et al.
Journal of Marine Science and Application (2025)
Open Access
Robust penalized extreme learning machine regression with applications in wind speed forecasting
Yang Yang, Hu Zhou, Yuchao Gao, et al.
Neural Computing and Applications (2021) Vol. 34, Iss. 1, pp. 391-407
Closed Access | Times Cited: 23
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, et al.
Journal of Physics Conference Series (2022) Vol. 2265, Iss. 2, pp. 022035-022035
Open Access | Times Cited: 10
Uncertain accessibility estimation method for offshore wind farm based on multi‐step probabilistic wave forecasting
Hao Zhang, Jie Yan, Shuang Han, et al.
IET Renewable Power Generation (2021) Vol. 15, Iss. 13, pp. 2944-2955
Open Access | Times Cited: 9
Forecasting the Total South African Unplanned Capability Loss Factor Using an Ensemble of Deep Learning Techniques
Sibonelo Motepe, Ali N. Hasan, Thokozani Shongwe
Energies (2022) Vol. 15, Iss. 7, pp. 2546-2546
Open Access | Times Cited: 4
Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, et al.
Journal of Physics Conference Series (2022) Vol. 2362, Iss. 1, pp. 012005-012005
Open Access | Times Cited: 4
A novel prediction method for low wind output processes under very few samples based on improved W‐DCGAN
Shihua Liu, Han Wang, Weiye Song, et al.
IET Renewable Power Generation (2024) Vol. 18, Iss. 14, pp. 2294-2304
Open Access
Research on fault causes and treatment methods of squirrel-cage asynchronous wind turbine
Xiuqi Zhang, Huifang Gao, Bin Cao
Journal of Physics Conference Series (2023) Vol. 2488, Iss. 1, pp. 012026-012026
Open Access | Times Cited: 1
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