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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 cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition
Hao Yin, Zuhong Ou, Shengquan Huang, et al.
Energy (2019) Vol. 189, pp. 116316-116316
Closed Access | Times Cited: 90

Showing 1-25 of 90 citing articles:

Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
K U Jaseena, Binsu C. Kovoor
Energy Conversion and Management (2021) Vol. 234, pp. 113944-113944
Closed Access | Times Cited: 236
A review and taxonomy of wind and solar energy forecasting methods based on deep learning
Ghadah Alkhayat, Rashid Mehmood
Energy and AI (2021) Vol. 4, pp. 100060-100060
Open Access | Times Cited: 205
An improved residual-based convolutional neural network for very short-term wind power forecasting
Ceyhun Yıldız, Hakan Açıkgöz, Deniz Korkmaz, et al.
Energy Conversion and Management (2020) Vol. 228, pp. 113731-113731
Closed Access | Times Cited: 198
A hybrid attention-based deep learning approach for wind power prediction
Zhengjing Ma, Gang Mei
Applied Energy (2022) Vol. 323, pp. 119608-119608
Closed Access | Times Cited: 100
A comprehensive review on deep learning approaches in wind forecasting applications
Zhou Wu, Gan Luo, Zhile Yang, et al.
CAAI Transactions on Intelligence Technology (2022) Vol. 7, Iss. 2, pp. 129-143
Open Access | Times Cited: 96
A short-term wind power forecasting method based on multivariate signal decomposition and variable selection
Ting Yang, Zhenning Yang, Fei Li, et al.
Applied Energy (2024) Vol. 360, pp. 122759-122759
Closed Access | Times Cited: 26
Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023)
Eghbal Hosseini, Abbas M. Al-Ghaili, Dler Hussein Kadir, et al.
Energy Strategy Reviews (2024) Vol. 53, pp. 101409-101409
Open Access | Times Cited: 17
Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm
Yingchao Dong, Hongli Zhang, Cong Wang, et al.
Neurocomputing (2021) Vol. 462, pp. 169-184
Closed Access | Times Cited: 92
A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization
Anbo Meng, Shun Chen, Zuhong Ou, et al.
Energy (2021) Vol. 238, pp. 121795-121795
Closed Access | Times Cited: 85
A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture
Hao Yin, Zuhong Ou, Jiajin Fu, et al.
Energy (2021) Vol. 234, pp. 121271-121271
Closed Access | Times Cited: 82
Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder
Lin Wang, Rui Tao, Huanling Hu, et al.
Renewable Energy (2020) Vol. 164, pp. 642-655
Closed Access | Times Cited: 79
Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power
Lingling Li, Zhifeng Liu, Ming‐Lang Tseng, et al.
Expert Systems with Applications (2021) Vol. 184, pp. 115579-115579
Open Access | Times Cited: 78
Multi-step prediction of photovoltaic power based on two-stage decomposition and BILSTM
Wenshuai Lin, Bin Zhang, Hongyi Li, et al.
Neurocomputing (2022) Vol. 504, pp. 56-67
Closed Access | Times Cited: 52
A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine
Anbo Meng, Zibin Zhu, Weisi Deng, et al.
Energy (2022) Vol. 260, pp. 124957-124957
Closed Access | Times Cited: 45
Wind power forecasting based on new hybrid model with TCN residual modification
Jiaojiao Zhu, Liancheng Su, Yingwei Li
Energy and AI (2022) Vol. 10, pp. 100199-100199
Open Access | Times Cited: 45
Tutorial on time series prediction using 1D-CNN and BiLSTM: A case example of peak electricity demand and system marginal price prediction
Jae-Dong Kim, S. Oh, Heesoo Kim, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106817-106817
Closed Access | Times Cited: 38
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
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: 24
An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division
Anbo Meng, Haitao Zhang, Zhongfu Dai, et al.
Energy (2024) Vol. 299, pp. 131383-131383
Closed Access | Times Cited: 8
Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning
Xin Liu, Zheming Cao, Zijun Zhang
Energy (2020) Vol. 217, pp. 119356-119356
Closed Access | Times Cited: 61
Current status of hybrid structures in wind forecasting
Mehrnaz Ahmadi, Mehdi Khashei
Engineering Applications of Artificial Intelligence (2020) Vol. 99, pp. 104133-104133
Closed Access | Times Cited: 54
A novel ensemble model for long-term forecasting of wind and hydro power generation
Priyanka Malhan, Monika Mittal
Energy Conversion and Management (2021) Vol. 251, pp. 114983-114983
Closed Access | Times Cited: 52
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: 37
A multimodal approach to chaotic renewable energy prediction using meteorological and historical information
Hui Hwang Goh, Ronghui He, Dongdong Zhang, et al.
Applied Soft Computing (2022) Vol. 118, pp. 108487-108487
Closed Access | Times Cited: 31
Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions
Yinsong Chen, Samson S. Yu, Shama Naz Islam, et al.
Energy Reports (2022) Vol. 8, pp. 8805-8820
Open Access | Times Cited: 30
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