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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:

A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model
Dongqin Zhang, Gang Hu, Jie Song, et al.
Energy (2023) Vol. 288, pp. 129823-129823
Closed Access | Times Cited: 11

Showing 11 citing articles:

A multi-factor clustering integration paradigm for wind speed point-interval prediction based on feature selection and optimized inverted transformer
Jujie Wang, Weiyi Jiang, Shuqin Shu, et al.
Energy (2025), pp. 135210-135210
Closed Access
A Flow Rate Estimation Method for Gas-liquid Two-Phase Flow Based on Transformer Neural Network
Yuxiao Jiang, Hao Wang, Yinyan Liu, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 16, pp. 26902-26913
Closed Access | Times Cited: 2
Enhancing Wind Power Forecasting Accuracy with Hybrid Deep Learning and Teaching-Learning-Based Optimization
Mohd Herwan Sulaiman, Zuriani Mustaffa
Cleaner Energy Systems (2024) Vol. 9, pp. 100139-100139
Open Access | Times Cited: 2
Predicting Energy Production in Renewable Energy Power Plants Using Deep Learning
Abdil Karakan
Energies (2024) Vol. 17, Iss. 16, pp. 4031-4031
Open Access | Times Cited: 2
A Hybrid Multivariate Multistep Wind-Speed Forecasting Model Based on a Deep-Learning Neural Network
D. H. Wei, Zhongda Tian
Journal of Energy Engineering (2024) Vol. 150, Iss. 6
Closed Access | Times Cited: 1
A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network
Yuxiao Jiang, Yinyan Liu, Lihui Peng, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 139, pp. 109593-109593
Closed Access | Times Cited: 1
Using stacking ensemble learning to predict multi-step wind speed based on wavelet transformation, two-steps feature selection method, and neural networks
Faezeh Amirteimoury, Gholamreza Memarzadeh, Farshid Keynia
Measurement (2024) Vol. 244, pp. 116500-116500
Closed Access | Times Cited: 1
A Novel Hybrid Wind Speed Prediction Model Utilizing High-Low Frequency Based on Gru-Informer
Tao Liang, Mengjing Chen, Jianxin Tan, et al.
(2024)
Closed Access
Using Stacking Ensemble Learning to Predict Multi-Step Wind Speed Based on Wavelet Transformation, a Two-Steps Feature Selection Method, and Neural Networks
Faezeh Amirteimoury, Gholamreza Memarzadeh, Farshid Keynia
(2024)
Closed Access
Deep learning-based extension of wind pressure time series
Biao Tong, Yang Liang, Jie Song, et al.
Journal of Wind Engineering and Industrial Aerodynamics (2024) Vol. 254, pp. 105909-105909
Closed Access
Emission Rate Estimation of Industrial Air Pollutant Emissions Based on Mobile Observation
Xinlei Cui, Yu Qi, Weichun Ma, et al.
Atmosphere (2024) Vol. 15, Iss. 8, pp. 969-969
Open Access
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