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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:

Wind power forecasting – A data-driven method along with gated recurrent neural network
Adam Kisvari, Zi Lin, Xiaolei Liu
Renewable Energy (2020) Vol. 163, pp. 1895-1909
Open Access | Times Cited: 277

Showing 1-25 of 277 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: 545
Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM
Xiaolei Liu, Zi Lin, Zi‐Ming Feng
Energy (2021) Vol. 227, pp. 120492-120492
Open Access | Times Cited: 307
Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)
Wanqing Zhang, Zi Lin, Xiaolei Liu
Renewable Energy (2021) Vol. 185, pp. 611-628
Open Access | Times Cited: 175
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
Zeni Zhao, Sining Yun, Lingyun Jia, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 105982-105982
Closed Access | Times Cited: 148
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
Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA)
Xuechen Li, Xinfang Ma, Fengchao Xiao, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 208, pp. 109309-109309
Closed Access | Times Cited: 130
Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model
Dongxiao Niu, Lijie Sun, Min Yu, et al.
Energy (2022) Vol. 254, pp. 124384-124384
Closed Access | Times Cited: 106
An Edge-AI Based Forecasting Approach for Improving Smart Microgrid Efficiency
Lingling Lv, Zongyu Wu, Lei Zhang, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 18, Iss. 11, pp. 7946-7954
Closed Access | Times Cited: 100
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: 96
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
Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery
Yi Guo, Bo Ming, Qiang Huang, et al.
Applied Energy (2022) Vol. 309, pp. 118467-118467
Closed Access | Times Cited: 90
Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis
Yulong Xiao, Chongzhe Zou, Hetian Chi, et al.
Energy (2022) Vol. 267, pp. 126503-126503
Closed Access | Times Cited: 88
Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization
Sheng-Xiang Lv, Lin Wang
Applied Energy (2022) Vol. 311, pp. 118674-118674
Closed Access | Times Cited: 87
Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects
Natei Ermias Benti, Mesfin Diro Chaka, Addisu Gezahegn Semie
Sustainability (2023) Vol. 15, Iss. 9, pp. 7087-7087
Open Access | Times Cited: 84
Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning
Farah Shahid, Wood David A., Nisar Humaira, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 167, pp. 112700-112700
Closed Access | Times Cited: 80
Hourly day ahead wind speed forecasting based on a hybrid model of EEMD, CNN-Bi-LSTM embedded with GA optimization
Thi Hoai Thu Nguyen, Quoc Bao Phan
Energy Reports (2022) Vol. 8, pp. 53-60
Open Access | Times Cited: 78
Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty
Lei Liu, Jicheng Liu, Yu Ye, et al.
Renewable Energy (2023) Vol. 205, pp. 598-607
Closed Access | Times Cited: 51
Advanced hyperparameter optimization of deep learning models for wind power prediction
Shahram Hanifi, Andrea Cammarono, Hossein Zare‐Behtash
Renewable Energy (2023) Vol. 221, pp. 119700-119700
Open Access | Times Cited: 51
Deep learning-based multistep ahead wind speed and power generation forecasting using direct method
Maryam Yaghoubirad, Narjes Azizi, Meisam Farajollahi, et al.
Energy Conversion and Management (2023) Vol. 281, pp. 116760-116760
Closed Access | Times Cited: 50
Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network
Jiayang Liu, Xiaosun Wang, Fuqi Xie, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 106000-106000
Closed Access | Times Cited: 45
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: 25
CNN–LSTM–AM: A power prediction model for offshore wind turbines
Yu Sun, Qibo Zhou, Li Sun, et al.
Ocean Engineering (2024) Vol. 301, pp. 117598-117598
Closed Access | Times Cited: 24
A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data
Zhaohua Liu, Chang-Tong Wang, Hua‐Liang Wei, et al.
Expert Systems with Applications (2024) Vol. 247, pp. 123237-123237
Closed Access | Times Cited: 18
A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting
Uğur Yüzgeç, Emrah Dokur, MEHMET EMİN BALCI
Energy (2024) Vol. 300, pp. 131546-131546
Closed Access | Times Cited: 17
Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns
Qiong Tang, Ishan Jha, Alireza Bahrami, et al.
Frontiers of Structural and Civil Engineering (2024) Vol. 18, Iss. 8, pp. 1169-1194
Closed Access | Times Cited: 16
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