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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:

Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model
Sheng-Xiang Lv, Lin Wang
Energy (2022) Vol. 263, pp. 126100-126100
Closed Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants
Stéfano Frizzo Stefenon, Laio Oriel Seman, Luiza Scapinello Aquino, et al.
Energy (2023) Vol. 274, pp. 127350-127350
Closed Access | Times Cited: 65
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
Wind power forecasting system with data enhancement and algorithm improvement
Yagang Zhang, Xue Kong, Jingchao Wang, et al.
Renewable and Sustainable Energy Reviews (2024) Vol. 196, pp. 114349-114349
Closed Access | Times Cited: 16
Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S
Xiaoying Sun, Haizhong Liu
Energy (2024) Vol. 305, pp. 132228-132228
Closed Access | Times Cited: 16
A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies
Jianzhou Wang, Xinsong Niu, Lifang Zhang, et al.
Expert Systems with Applications (2023) Vol. 241, pp. 122487-122487
Closed Access | Times Cited: 36
Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables
Wenjun Jiang, Bo Liu, Yang Liang, et al.
Applied Energy (2023) Vol. 353, pp. 122155-122155
Closed Access | Times Cited: 32
Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China
Chengqing Yu, Guangxi Yan, Chengming Yu, et al.
Applied Soft Computing (2023) Vol. 148, pp. 110864-110864
Closed Access | Times Cited: 22
Rolling decomposition method in fusion with echo state network for wind speed forecasting
Huanling Hu, Lin Wang, Dabin Zhang, et al.
Renewable Energy (2023) Vol. 216, pp. 119101-119101
Closed Access | Times Cited: 21
Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model
Lionel Joseph, Ravinesh C. Deo, David Casillas-Pérez, et al.
Applied Energy (2024) Vol. 359, pp. 122624-122624
Open Access | Times Cited: 13
Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning
Hamidreza Eskandari, Hassan Saadatmand, Muhammad Ramzan, et al.
Applied Energy (2024) Vol. 366, pp. 123314-123314
Open Access | Times Cited: 9
A robust chaos-inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed forecasting
Caner Barış, Cağfer Yanarateş, Aytaç Altan
PeerJ Computer Science (2024) Vol. 10, pp. e2393-e2393
Open Access | Times Cited: 9
Deep learning in public health: Comparative predictive models for COVID-19 case forecasting
Muhammad Usman Tariq, Shuhaida Ismail
PLoS ONE (2024) Vol. 19, Iss. 3, pp. e0294289-e0294289
Open Access | Times Cited: 7
A comparative study on effect of news sentiment on stock price prediction with deep learning architecture
Keshab R. Dahal, Nawa Raj Pokhrel, Santosh Gaire, et al.
PLoS ONE (2023) Vol. 18, Iss. 4, pp. e0284695-e0284695
Open Access | Times Cited: 17
An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
Qingyao Qiao, Hamidreza Eskandari, Hassan Saadatmand, et al.
Energy (2023) Vol. 286, pp. 129499-129499
Open Access | Times Cited: 16
Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network
Guowei Zhang, Yi Zhang, Hui Wang, et al.
Energy (2023) Vol. 288, pp. 129618-129618
Closed Access | Times Cited: 16
Comprehensive commodity price forecasting framework using text mining methods
Wuyue An, Lin Wang, Dongfeng Zhang
Journal of Forecasting (2023) Vol. 42, Iss. 7, pp. 1865-1888
Closed Access | Times Cited: 15
Machine learning and the cross-section of cryptocurrency returns
Nusret Cakici, Syed Jawad Hussain Shahzad, Barbara Będowska-Sójka, et al.
International Review of Financial Analysis (2024) Vol. 94, pp. 103244-103244
Closed Access | Times Cited: 6
TCAMS-Trans: Efficient temporal-channel attention multi-scale transformer for net load forecasting
Qingyong Zhang, Shiyang Zhou, Bingrong Xu, et al.
Computers & Electrical Engineering (2024) Vol. 118, pp. 109415-109415
Closed Access | Times Cited: 5
A multi-stage multivariable offshore wind speed assessment system incorporating statistical analysis, decomposition, feature selection, bias correction, and attention-based hybrid prediction
Jingjing Cai, Cheng Peng, Li Xu, et al.
Wind Engineering (2025)
Closed Access
Optimizing Stock Price Prediction for South Asian Markets Using LSTM, GRU, CNN with Greedy Algorithm
Bushra Saeed, Wei Yin
Research Square (Research Square) (2025)
Closed Access
Enhanced forecasting method for realized volatility of energy futures prices: A secondary decomposition-based deep learning model
Hao Gong, H. Y. Xing, Qianwen Wang
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110321-110321
Closed Access
Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks
Zhou Wu, Shaoxiong Zeng, Ruiqi Jiang, et al.
Energy (2023) Vol. 270, pp. 126906-126906
Closed Access | Times Cited: 13
An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer
Yuqian Tian, Dazhi Wang, Guolin Zhou, et al.
Entropy (2023) Vol. 25, Iss. 4, pp. 647-647
Open Access | Times Cited: 13
A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power
Xinjie Shi, Jianzhou Wang, Bochen Zhang
Applied Energy (2023) Vol. 353, pp. 122015-122015
Closed Access | Times Cited: 13
A contrastive learning-based framework for wind power forecast
Nanyang Zhu, Zemei Dai, Ying Wang, et al.
Expert Systems with Applications (2023) Vol. 230, pp. 120619-120619
Closed Access | Times Cited: 12
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