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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:

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

Showing 26-50 of 198 citing articles:

Power prediction of a wind farm cluster based on spatiotemporal correlations
Jiaan Zhang, Dong Liu, Zhijun Li, et al.
Applied Energy (2021) Vol. 302, pp. 117568-117568
Closed Access | Times Cited: 75
Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects
Molla Shahadat Hossain Lipu, Md. Sazal Miah, M. A. Hannan, et al.
IEEE Access (2021) Vol. 9, pp. 102460-102489
Open Access | Times Cited: 61
A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries
Runmin Zou, Yuxin Duan, Yun Wang, et al.
Journal of Energy Storage (2022) Vol. 57, pp. 106298-106298
Closed Access | Times Cited: 50
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
A Cyber-Secure generalized supermodel for wind power forecasting based on deep federated learning and image processing
Hamed Moayyed, Arash Moradzadeh, Behnam Mohammadi‐Ivatloo, et al.
Energy Conversion and Management (2022) Vol. 267, pp. 115852-115852
Closed Access | Times Cited: 44
An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting
Huixin Ma, Chu Zhang, Peng Tian, et al.
Energy (2022) Vol. 256, pp. 124650-124650
Closed Access | Times Cited: 41
Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps
Baihao Qiao, Jing Liu, Peng Wu, et al.
Applied Soft Computing (2022) Vol. 129, pp. 109586-109586
Closed Access | Times Cited: 40
Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field
Zhen Shao, Jun Han, Wei Zhao, et al.
Energy Conversion and Management (2022) Vol. 269, pp. 116138-116138
Closed Access | Times Cited: 40
An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting
Xiaoran Dai, Shuai Liu, Wenshan Hu
Energy (2023) Vol. 272, pp. 127173-127173
Closed Access | Times Cited: 39
Short-term wind power prediction method based on deep clustering-improved Temporal Convolutional Network
Yiwei Sheng, Han Wang, Jie Yan, et al.
Energy Reports (2023) Vol. 9, pp. 2118-2129
Open Access | Times Cited: 30
Evolving long short-term memory neural network for wind speed forecasting
Cong Huang, Hamid Reza Karimi, Peng Mei, et al.
Information Sciences (2023) Vol. 632, pp. 390-410
Open Access | Times Cited: 30
An improved Wavenet network for multi-step-ahead wind energy forecasting
Yun Wang, Tuo Chen, Shengchao Zhou, et al.
Energy Conversion and Management (2023) Vol. 278, pp. 116709-116709
Closed Access | Times Cited: 26
A Review of Modern Wind Power Generation Forecasting Technologies
Wen-Chang Tsai, Chih-Ming Hong, Chia‐Sheng Tu, et al.
Sustainability (2023) Vol. 15, Iss. 14, pp. 10757-10757
Open Access | Times Cited: 26
GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations
Nanyang Zhu, Ying Wang, Kun Yuan, et al.
Applied Energy (2024) Vol. 364, pp. 123194-123194
Closed Access | Times Cited: 13
Investigating power loss in a wind turbine using real-time vibration signature
Vishwaas Narasinh, Prateek Mital, Nilanjan Chakravortty, et al.
Engineering Failure Analysis (2024) Vol. 159, pp. 108010-108010
Closed Access | Times Cited: 11
Combined electricity load-forecasting system based on weighted fuzzy time series and deep neural networks
Zhining Cao, Jianzhou Wang, Yurui Xia
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 108375-108375
Closed Access | Times Cited: 9
An effective MPPT control based on machine learning method for proton exchange membrane fuel cell systems
Beşir Dandıl, Hakan Açıkgöz, Resul Çöteli̇
International Journal of Hydrogen Energy (2024) Vol. 75, pp. 344-353
Closed Access | Times Cited: 8
A Novel Supercapacitor Degradation Prediction Using a 1D Convolutional Neural Network and Improved Informer Model
Hao Zhang, Zhenxiao Yi, Le Kang, et al.
Protection and Control of Modern Power Systems (2024) Vol. 9, Iss. 4, pp. 51-68
Open Access | Times Cited: 8
Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study
Fouzi Harrou, Abdelkader Dairi, Abdelhakim Dorbane, et al.
Results in Engineering (2024) Vol. 23, pp. 102504-102504
Open Access | Times Cited: 8
Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features
Gang Li, Chen Lin, Yupeng Li
Energies (2025) Vol. 18, Iss. 3, pp. 652-652
Open Access | Times Cited: 1
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
New Hybrid Deep Neural Architectural Search-Based Ensemble Reinforcement Learning Strategy for Wind Power Forecasting
Seyed Mohammad Jafar Jalali, Gerardo J. Osório, Sajad Ahmadian, et al.
IEEE Transactions on Industry Applications (2021) Vol. 58, Iss. 1, pp. 15-27
Closed Access | Times Cited: 51
Data-augmented sequential deep learning for wind power forecasting
Hao Chen, Yngve Birkelund, Qixia Zhang
Energy Conversion and Management (2021) Vol. 248, pp. 114790-114790
Open Access | Times Cited: 49
A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting
Jie Liu, Quan Shi, Ruilian Han, et al.
Energies (2021) Vol. 14, Iss. 20, pp. 6500-6500
Open Access | Times Cited: 41
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

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