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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 two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data
Hong Liu, Luoxiao Yang, Bingying Zhang, et al.
Energy (2023) Vol. 283, pp. 128510-128510
Closed Access | Times Cited: 25

Showing 25 citing articles:

Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism
Mao Yang, Y. Huang, Yunfeng Guo, et al.
Energy (2024) Vol. 290, pp. 130238-130238
Closed Access | Times Cited: 13
Short-Term Power Forecasting of Wind Farm Cluster Based on Global Information Adaptive Perceptual Graph Convolution Network
Mao Yang, Chaoyi Ju, Y. Huang, et al.
IEEE Transactions on Sustainable Energy (2024) Vol. 15, Iss. 3, pp. 2063-2076
Closed Access | Times Cited: 11
A comprehensive review on the development of data-driven methods for wind power prediction and AGC performance evaluation in wind–thermal bundled power systems
Shuai Wang, Bin Li, Guanzheng Li, et al.
Energy and AI (2024) Vol. 16, pp. 100336-100336
Open Access | Times Cited: 8
A comprehensive wind power prediction system based on correct multiscale clustering ensemble, similarity matching, and improved whale optimization algorithm–A case study in China
Chunsheng Yu
Renewable Energy (2025), pp. 122529-122529
Closed Access
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
Load Equipment Segmentation and Assessment Method Based on Multi-Source Tensor Feature Fusion
Xiaoli Zhang, Congcong Zhao, Wenjie Lu, et al.
Electronics (2025) Vol. 14, Iss. 5, pp. 1040-1040
Open Access
A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN
Anbo Meng, Hai‐Tao Zhang, Hao Yin, et al.
Energy (2023) Vol. 283, pp. 129139-129139
Closed Access | Times Cited: 14
Development and trending of deep learning methods for wind power predictions
Hong Liu, Zijun Zhang
Artificial Intelligence Review (2024) Vol. 57, Iss. 5
Open Access | Times Cited: 4
Ultra-short-term wind power forecasting based on feature weight analysis and cluster dynamic division
Chen Chang, Yuyu Meng, Jiuyuan Huo, et al.
Journal of Renewable and Sustainable Energy (2024) Vol. 16, Iss. 2
Closed Access | Times Cited: 3
Short-term wind power forecasting using integrated boosting approach
Ubaid Ahmed, Rasheed Muhammad, Syed Sami Abbas, et al.
Frontiers in Energy Research (2024) Vol. 12
Open Access | Times Cited: 3
Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems
Mostafa A. Rushdi, Shigeo Yoshida, Koichi Watanabe, et al.
Energies (2024) Vol. 17, Iss. 15, pp. 3630-3630
Open Access | Times Cited: 3
Very short-term wind power forecasting considering static data: An improved transformer model
Sen Wang, Yonghui Sun, Wenjie Zhang, et al.
Energy (2024), pp. 133577-133577
Closed Access | Times Cited: 3
Wind power output prediction in complex terrain based on modal decomposition attentional convolutional network
Yang Liu, Pingping Xie, Yinguo Yang, et al.
Frontiers in Energy Research (2024) Vol. 11
Open Access | Times Cited: 1
Graph-Based Large Scale Probabilistic PV Power Forecasting Insensitive to Space-Time Missing Data
Keunju Song, Minsoo Kim, Hongseok Kim
IEEE Transactions on Sustainable Energy (2024) Vol. 16, Iss. 1, pp. 160-173
Closed Access | Times Cited: 1
Quasi-Newton Optimized Kolmogorov-Arnold Networks for Wind Farm Power Prediction
Auwalu Saleh Mubarak, Zubaida Said Ameen, Sagiru Mati, et al.
Heliyon (2024) Vol. 10, Iss. 23, pp. e40799-e40799
Open Access | Times Cited: 1
Enhancing Wind Energy Predictions: A Deep Neural Network Approach to Optimizing Production and Maintenance
Carlos Quiterio Gómez Muñoz, Javier Sanchez Soriano, Pedro Jose Paniagua Falo
(2024)
Closed Access
Short-Term Wind Power Prediction Based on a Variational Mode Decomposition–BiTCN–Psformer Hybrid Model
Wu Xu, Wenjing Dai, Dongyang Li, et al.
Energies (2024) Vol. 17, Iss. 16, pp. 4089-4089
Open Access
Probabilistic Forecasting of Multiple Plant Day-ahead Renewable Power Generation Sequences with Data Privacy Preserving
Hong Liu, Zijun Zhang
Energy and AI (2024), pp. 100463-100463
Open Access
Leveraging Deep Learning Architectures for Accurate Wind Speed and Power Prediction in Renewable Energy Systems
V Alekhya, R J Anandhi, Alok Jain, et al.
(2024), pp. 656-660
Closed Access
Integrating spatio-positional series attention to deep network for multi-turbine short-term wind power prediction
Qianyue Wang, Gangquan Si, Kai Qu, et al.
Journal of Renewable and Sustainable Energy (2024) Vol. 16, Iss. 1
Open Access
A hybrid deep learning framework for predicting industrial wastewater influent quality based on graph optimisation
Jiafei Cao, Anke Xue, Yong Yang, et al.
Journal of Water Process Engineering (2024) Vol. 65, pp. 105831-105831
Closed Access
A short-term wind power prediction method via self-adaptive adjacency matrix and spatiotemporal graph neural networks
Yang Xie, Jianyong Zheng, Gareth Taylor, et al.
Computers & Electrical Engineering (2024) Vol. 120, pp. 109715-109715
Closed Access
Wind Power Curve Model Combining Smoothed Spline with First-Order Moments and Density-Adjusted Wind Speed Strategy
Tianhao Liu, Kunye Lv, Fengjie Chen, et al.
Energy (2024), pp. 133628-133628
Closed Access
Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification
Guili Ding, Gaoyang Yan, Zongyao Wang, et al.
Frontiers in Energy Research (2023) Vol. 11
Open Access | Times Cited: 1
A Comprehensive Wind Power Prediction System Based on Correct Multiscale Clustering Ensemble, Similarity Matching, and Improved Whale Optimization Algorithm--A Case Study in China
Chunsheng Yu
(2023)
Closed Access
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