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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:

Day-ahead wind power forecasting based on the clustering of equivalent power curves
Mao Yang, Chaoyu Shi, Huiyu Liu
Energy (2020) Vol. 218, pp. 119515-119515
Closed Access | Times Cited: 104

Showing 1-25 of 104 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
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
Upma Singh, M. Rizwan, Muhannad Alaraj, et al.
Energies (2021) Vol. 14, Iss. 16, pp. 5196-5196
Open Access | Times Cited: 134
New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight
Erlong Zhao, Shaolong Sun, Shouyang Wang
Data Science and Management (2022) Vol. 5, Iss. 2, pp. 84-95
Open Access | Times Cited: 118
Wind Energy Scenario, Success and Initiatives towards Renewable Energy in India—A Review
Upma Singh, M. Rizwan, Hasmat Malik, et al.
Energies (2022) Vol. 15, Iss. 6, pp. 2291-2291
Open Access | Times Cited: 94
Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process
Mao Yang, Yunfeng Guo, Y. Huang
Energy (2023) Vol. 282, pp. 128947-128947
Closed Access | Times Cited: 50
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
Investigating the Impact of Data Normalization Methods on Predicting Electricity Consumption in a Building Using different Artificial Neural Network Models.
Yang‐Seon Kim, Moon Keun Kim, Nuodi Fu, et al.
Sustainable Cities and Society (2024), pp. 105570-105570
Open Access | Times Cited: 18
A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information
Mao Yang, Chao Han, Wei Zhang, et al.
Energy (2024) Vol. 294, pp. 130770-130770
Closed Access | Times Cited: 16
Two-stage Day-ahead Multi-step Prediction of Wind Power Considering Time-series Information Interaction
Mao Yang, Xiangyu Li, Fulin Fan, et al.
Energy (2024) Vol. 312, pp. 133580-133580
Closed Access | Times Cited: 15
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: 14
Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory
Mahsa Dehghan Manshadi, Majid Ghassemi, Milad Mousavi, et al.
Energies (2021) Vol. 14, Iss. 16, pp. 4867-4867
Open Access | Times Cited: 70
Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm
Xuefang Xu, Shiting Hu, Peiming Shi, et al.
Energy (2022) Vol. 262, pp. 125342-125342
Closed Access | Times Cited: 57
Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting
Mao Yang, Da Wang, Chuanyu Xu, et al.
Renewable Energy (2023) Vol. 211, pp. 582-594
Closed Access | Times Cited: 35
A short-term wind power prediction method based on dynamic and static feature fusion mining
Mao Yang, Da Wang, Wei Zhang
Energy (2023) Vol. 280, pp. 128226-128226
Closed Access | Times Cited: 28
Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM
Zheng Li, Ruosi Xu, Xiaorui Luo, et al.
Energy Reports (2023) Vol. 9, pp. 6449-6460
Open Access | Times Cited: 23
Onshore and offshore wind power generation forecasting using a novel flexible time-varying fractional nonlinear grey model
Xuemei Li, Shiwei Zhou, Yufeng Zhao
Energy Conversion and Management (2023) Vol. 297, pp. 117695-117695
Closed Access | Times Cited: 21
A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation
Guolian Hou, Junjie Wang, Yuzhen Fan, et al.
Renewable Energy (2024) Vol. 226, pp. 120360-120360
Closed Access | Times Cited: 12
Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy
Xiaodi Wang, Hao Yan, Wendong Yang
Energy (2024) Vol. 297, pp. 131142-131142
Closed Access | Times Cited: 11
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
GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction
Shengmao Lin, Shu Wang, Xuefang Xu, et al.
Energy (2024) Vol. 292, pp. 130404-130404
Closed Access | Times Cited: 9
Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating
Shengyuan Zhong, Xiaoyuan Wang, Jun Zhao, et al.
Applied Energy (2021) Vol. 288, pp. 116623-116623
Closed Access | Times Cited: 48
Wind Power Prediction Based on Machine Learning and Deep Learning Models
Zahraa Tarek, Mahmoud Y. Shams, Ahmed M. Elshewey, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2022) Vol. 74, Iss. 1, pp. 715-732
Open Access | Times Cited: 35
Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method
Huijing Fan, Zhao Zhen, Nian Liu, et al.
Energy (2022) Vol. 266, pp. 126420-126420
Closed Access | Times Cited: 35
Use machine learning algorithms to predict turbine power generation to replace renewable energy with fossil fuels
Seyed Matin Malakouti
Energy Exploration & Exploitation (2022) Vol. 41, Iss. 2, pp. 836-857
Open Access | Times Cited: 31
Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes
Lidong Zhang, Zhengcong Feng, Yuze Zhao, et al.
Journal of Marine Science and Engineering (2024) Vol. 12, Iss. 3, pp. 434-434
Open Access | Times Cited: 6
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