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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:

Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal
Yong Qin, Kun Li, Zhanhao Liang, et al.
Applied Energy (2018) Vol. 236, pp. 262-272
Closed Access | Times Cited: 120

Showing 1-25 of 120 citing articles:

A review of deep learning for renewable energy forecasting
Huaizhi Wang, Zhenxing Lei, Xian Zhang, et al.
Energy Conversion and Management (2019) Vol. 198, pp. 111799-111799
Closed Access | Times Cited: 861
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 review on renewable energy and electricity requirement forecasting models for smart grid and buildings
Tanveer Ahmad, Hongcai Zhang, Biao Yan
Sustainable Cities and Society (2020) Vol. 55, pp. 102052-102052
Closed Access | Times Cited: 368
Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model
Xuanyi Song, Yuetian Liu, Liang Xue, et al.
Journal of Petroleum Science and Engineering (2019) Vol. 186, pp. 106682-106682
Closed Access | Times Cited: 358
Wind power forecasting based on daily wind speed data using machine learning algorithms
Halil Demolli, Ahmet Şakir Dokuz, Alper Ecemiş, et al.
Energy Conversion and Management (2019) Vol. 198, pp. 111823-111823
Closed Access | Times Cited: 351
Photovoltaic power forecasting based LSTM-Convolutional Network
Kejun Wang, Xiaoxia Qi, Hongda Liu
Energy (2019) Vol. 189, pp. 116225-116225
Closed Access | Times Cited: 342
Machine learning driven smart electric power systems: Current trends and new perspectives
Muhammad Sohail Ibrahim, Wei Dong, Qiang Yang
Applied Energy (2020) Vol. 272, pp. 115237-115237
Closed Access | Times Cited: 300
A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
Ying‐Yi Hong, Christian Lian Paulo P. Rioflorido
Applied Energy (2019) Vol. 250, pp. 530-539
Closed Access | Times Cited: 274
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: 202
Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting
Pratima Kumari, Durga Toshniwal
Applied Energy (2021) Vol. 295, pp. 117061-117061
Closed Access | Times Cited: 187
Deep learning methods and applications for electrical power systems: A comprehensive review
Asiye Kaymaz Özcanlı, Fatma Yaprakdal, Mustafa Baysal
International Journal of Energy Research (2020) Vol. 44, Iss. 9, pp. 7136-7157
Open Access | Times Cited: 179
Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model
Yongqi Liu, Hui Qin, Zhendong Zhang, et al.
Applied Energy (2019) Vol. 260, pp. 114259-114259
Closed Access | Times Cited: 169
Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history
Yaoran Chen, Zhikun Dong, Yan Wang, et al.
Energy Conversion and Management (2020) Vol. 227, pp. 113559-113559
Closed Access | Times Cited: 142
Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM
Qianyu Wu, Fei Guan, Chen Lv, et al.
IET Renewable Power Generation (2021) Vol. 15, Iss. 5, pp. 1019-1029
Open Access | Times Cited: 134
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
Load Forecasting Techniques for Power System: Research Challenges and Survey
Naqash Ahmad, Yazeed Yasin Ghadi, Muhammad Adnan, et al.
IEEE Access (2022) Vol. 10, pp. 71054-71090
Open Access | Times Cited: 132
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
Peng Lu, Lin Ye, Yongning Zhao, et al.
Applied Energy (2021) Vol. 301, pp. 117446-117446
Closed Access | Times Cited: 129
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: 92
Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function
Bala Saibabu Bommidi, Kiran Teeparthi, Vishalteja Kosana
Energy (2022) Vol. 265, pp. 126383-126383
Closed Access | Times Cited: 83
Short-term wind power forecasting based on SSA-VMD-LSTM
Xiao-Zhi Gao, Wang Guo, Chunxiao Mei, et al.
Energy Reports (2023) Vol. 9, pp. 335-344
Open Access | Times Cited: 50
Multi‐step wind power forecast based on VMD‐LSTM
Li Han, Rongchang Zhang, Xuesong Wang, et al.
IET Renewable Power Generation (2019) Vol. 13, Iss. 10, pp. 1690-1700
Closed Access | Times Cited: 137
Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
Viet‐Ha Nhu, Nhat‐Duc Hoang, Hieu Nguyen, et al.
CATENA (2020) Vol. 188, pp. 104458-104458
Closed Access | Times Cited: 136
Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis
Zexian Sun, Mingyu Zhao
IEEE Access (2020) Vol. 8, pp. 134422-134434
Open Access | Times Cited: 115
Wind speed forecasting based on Quantile Regression Minimal Gated Memory Network and Kernel Density Estimation
Zhendong Zhang, Hui Qin, Yongqi Liu, et al.
Energy Conversion and Management (2019) Vol. 196, pp. 1395-1409
Closed Access | Times Cited: 110
A review of applications of artificial intelligent algorithms in wind farms
Yirui Wang, Yang Yu, Shuyang Cao, et al.
Artificial Intelligence Review (2019) Vol. 53, Iss. 5, pp. 3447-3500
Closed Access | Times Cited: 106
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