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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:

Multiperiod‐Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning
Lei Chen, Zhijun Li, Yi Zhang
Mathematical Problems in Engineering (2019) Vol. 2019, Iss. 1
Open Access | Times Cited: 14

Showing 14 citing articles:

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: 93
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107559-107559
Closed Access | Times Cited: 57
Review on Deep Learning Research and Applications in Wind and Wave Energy
Chengcheng Gu, Hua Li
Energies (2022) Vol. 15, Iss. 4, pp. 1510-1510
Open Access | Times Cited: 50
Deep Learning for Variable Renewable Energy: A Systematic Review
Janice Klaiber, Clemens van Dinther
ACM Computing Surveys (2023) Vol. 56, Iss. 1, pp. 1-37
Closed Access | Times Cited: 13
A hybrid wind speed forecasting model using stacked autoencoder and LSTM
K U Jaseena, Binsu C. Kovoor
Journal of Renewable and Sustainable Energy (2020) Vol. 12, Iss. 2
Closed Access | Times Cited: 35
Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks
Ashapurna Marndi, G.K. Patra, K. C. Gouda
Bulletin of Atmospheric Science and Technology (2020) Vol. 1, Iss. 1, pp. 91-108
Closed Access | Times Cited: 30
Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory
Moslem Imani, Hoda Fakour, Wen-Hau Lan, et al.
Atmosphere (2021) Vol. 12, Iss. 7, pp. 924-924
Open Access | Times Cited: 16
Research on ensemble model of anomaly detection based on autoencoder
Yaning Han, Yunyun Ma, Jinbo Wang, et al.
(2020), pp. 414-417
Closed Access | Times Cited: 13
Homogenized Point Mutual Information and Deep Quantum Reinforced Wind Power Prediction
W. G. Jency, J. E. Judith
International Transactions on Electrical Energy Systems (2022) Vol. 2022, pp. 1-15
Open Access | Times Cited: 8
Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm
Fang Yao, Wei Liu, Xingyong Zhao, et al.
Complexity (2020) Vol. 2020, pp. 1-12
Open Access | Times Cited: 11
Short Term Renewable Energy Forecasting with Deep Learning Neural Networks
V. Miroshnyk, Pavlo Shymaniuk, Viktoriia Sychova
Studies in systems, decision and control (2021), pp. 121-142
Closed Access | Times Cited: 8
Short-Term Wind Speed Forecasting Using Ensemble Learning
M. Karthikeyan, R. Rengaraj
(2021), pp. 502-506
Closed Access | Times Cited: 6
Forecasting Model of Wind Speed and Direction by Convolutional Neural Network - Deep Convolutional Long Short Term Memory
Anggraini Puspita Sari, Dwi Arman Prasetya, Takashi Yasuno, et al.
(2022), pp. 200-205
Closed Access | Times Cited: 2
A Novel Integration of Hodrick-Prescott Filter and Harmonic Analysis with Machine Learning Methods to Enhance Time Series Prediction Accuracy of Daily and Monthly Wind Speeds
Chigbogu G. Ozoegwu
Research Square (Research Square) (2021)
Open Access
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