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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:

A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism
Junheng Pang, Sheng Dong
Energy Conversion and Management (2024) Vol. 307, pp. 118343-118343
Closed Access | Times Cited: 21

Showing 21 citing articles:

A hybrid wind speed forecasting model with two-stage data processing based on adaptive neuro-fuzzy inference systems and deep learning algorithms
Zhongda Tian, D. H. Wei
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
A new paradigm based on Wasserstein Generative Adversarial Network and time-series graph for integrated energy system forecasting
Zhirui Tian, Gai Mei
Energy Conversion and Management (2025) Vol. 326, pp. 119484-119484
Closed Access | Times Cited: 1
Incorporating key features from structured and unstructured data for enhanced carbon trading price forecasting with interpretability analysis
Ming Jiang, Jinxing Che, Shuying Li, et al.
Applied Energy (2025) Vol. 382, pp. 125301-125301
Closed Access
WindForecastX: a dynamic approach for accurate long-term wind speed prediction in wind energy applications
Sasi Rekha Sankar, P. Madhavan
Ocean Dynamics (2025) Vol. 75, Iss. 1
Closed Access
A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model
Jie Du, S. C. Chen, Linlin Pan, et al.
Energies (2025) Vol. 18, Iss. 5, pp. 1136-1136
Open Access
Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition
Zhiyuan Leng, Chen Lü, Bin Yi, et al.
Energy (2025), pp. 135551-135551
Closed Access
A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
Jingxuan Wu, Shuting Li, Juan C. Vásquez, et al.
Energy Conversion and Management X (2024) Vol. 23, pp. 100650-100650
Open Access | Times Cited: 4
A synchronized multi-step wind speed prediction with adaptive features and parameters selection: Insights from an interaction model
Wenxin Xia, Jinxing Che, Kun Hu, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124764-124764
Closed Access | Times Cited: 4
A new multi-objective ensemble wind speed forecasting system: Mixed-frequency interval-valued modeling paradigm
Wendong Yang, Xinyi Zang, C.L. Wu, et al.
Energy (2024) Vol. 304, pp. 131963-131963
Closed Access | Times Cited: 3
Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa
Senthil Krishnamurthy, Oludamilare Bode Adewuyi, Emmanuel Luwaca, et al.
Energy Conversion and Management X (2024) Vol. 24, pp. 100772-100772
Open Access | Times Cited: 3
A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection
Zhihao Shang, Yanhua Chen, Daokai Lai, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124560-124560
Closed Access | Times Cited: 2
An integrated prediction model based on meta ensemble learning for short‐term wind speed forecasting
Zhengwei Ma, Ting Wu, Sensen Guo, et al.
IET Renewable Power Generation (2024)
Open Access | Times Cited: 1
Data denoising and deep learning prediction for the wind speed based on NOA optimization
Xinyi Xu, Shaojuan Ma, Cheng Huang
Research Square (Research Square) (2024)
Closed Access | Times Cited: 1
A novel analysis of random forest regression model for wind speed forecasting
J Sathyaraj, V. Sankardoss
Cogent Engineering (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 1
A singular spectrum analysis-enhanced BiTCN-selfattention model for runoff prediction
Wenchuan Wang, Feng-rui Ye, Yiyang Wang, et al.
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Using stacking ensemble learning to predict multi-step wind speed based on wavelet transformation, two-steps feature selection method, and neural networks
Faezeh Amirteimoury, Gholamreza Memarzadeh, Farshid Keynia
Measurement (2024) Vol. 244, pp. 116500-116500
Closed Access | Times Cited: 1
Using Stacking Ensemble Learning to Predict Multi-Step Wind Speed Based on Wavelet Transformation, a Two-Steps Feature Selection Method, and Neural Networks
Faezeh Amirteimoury, Gholamreza Memarzadeh, Farshid Keynia
(2024)
Closed Access
A Comprehensive Framework of the Decomposition-Based Hybrid Method for Ultra-Short-Term Wind Power Forecasting with On-Site Applicationon
Shixi Yang, Jiaxuan Zhou, Xiwen Gu, et al.
(2024)
Closed Access
A Comprehensive Framework of the Decomposition-Based Hybrid Method for Ultra-Short-Term Wind Power Forecasting with On-Site Application
Shixi Yang, Jiaxuan Zhou, Xiwen Gu, et al.
(2024)
Closed Access
An improved hybrid model for wind power forecasting through fusion of deep learning and adaptive online learning
Xinwei Zhao, Hai Peng Liu, Huaiping Jin, et al.
Computers & Electrical Engineering (2024) Vol. 120, pp. 109768-109768
Closed Access
A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application
Shixi Yang, Jiaxuan Zhou, Xiwen Gu, et al.
Energy (2024), pp. 133911-133911
Closed Access
An innovative memory-enhanced Elman neural network-based selective ensemble system for short-term wind speed prediction
Xue-Yi Ai, Tao Feng, Gan Wei, et al.
Applied Energy (2024) Vol. 380, pp. 125108-125108
Closed Access
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