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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:

Very short-term forecasting of wind power generation using hybrid deep learning model
Md. Alamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah, et al.
Journal of Cleaner Production (2021) Vol. 296, pp. 126564-126564
Open Access | Times Cited: 128

Showing 1-25 of 128 citing articles:

A review of very short-term wind and solar power forecasting
Rosemary Tawn, Jethro Browell
Renewable and Sustainable Energy Reviews (2021) Vol. 153, pp. 111758-111758
Open Access | Times Cited: 185
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: 163
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, et al.
International Journal of Electrical Power & Energy Systems (2021) Vol. 136, pp. 107712-107712
Closed Access | Times Cited: 121
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: 109
Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting
Mohammed A. A. Al‐qaness, Ahmed A. Ewees, Hong Fan, et al.
Applied Energy (2022) Vol. 314, pp. 118851-118851
Closed Access | Times Cited: 103
Optimized Forecasting Model to Improve the Accuracy of Very Short-Term Wind Power Prediction
Md. Alamgir Hossain, Evan Gray, Junwei Lu, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 19, Iss. 10, pp. 10145-10159
Open Access | Times Cited: 47
Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, et al.
Applied Intelligence (2024) Vol. 54, Iss. 4, pp. 3119-3134
Closed Access | Times Cited: 23
Key technologies for smart energy systems: Recent developments, challenges, and research opportunities in the context of carbon neutrality
Hongyu Zhu, Hui Hwang Goh, Dongdong Zhang, et al.
Journal of Cleaner Production (2021) Vol. 331, pp. 129809-129809
Closed Access | Times Cited: 93
A comparative analysis to forecast carbon dioxide emissions
Md. Omer Faruque, Md. Afser Jani Rabby, Md. Alamgir Hossain, et al.
Energy Reports (2022) Vol. 8, pp. 8046-8060
Open Access | Times Cited: 63
Computationally expedient Photovoltaic power Forecasting: A LSTM ensemble method augmented with adaptive weighting and data segmentation technique
Razin Ahmed, Victor Sreeram, Roberto Togneri, et al.
Energy Conversion and Management (2022) Vol. 258, pp. 115563-115563
Closed Access | Times Cited: 56
A wind power forecasting method based on optimized decomposition prediction and error correction
Jun Li, Shuqing Zhang, Zhenning Yang
Electric Power Systems Research (2022) Vol. 208, pp. 107886-107886
Closed Access | Times Cited: 50
Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems
Muhammad Hamza Zafar, Noman Mujeeb Khan, Majad Mansoor, et al.
Energy Conversion and Management (2022) Vol. 258, pp. 115564-115564
Open Access | Times Cited: 49
Short term wind energy prediction model based on data decomposition and optimized LSSVM
Yagang Zhang, Ruixuan Li
Sustainable Energy Technologies and Assessments (2022) Vol. 52, pp. 102025-102025
Closed Access | Times Cited: 41
A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network
Tianhong Liu, Shengli Qi, Xianzhu Qiao, et al.
Energy (2023) Vol. 288, pp. 129904-129904
Closed Access | Times Cited: 27
Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework
Yugui Tang, Kuo Yang, Shujing Zhang, et al.
Energy (2023) Vol. 278, pp. 127864-127864
Closed Access | Times Cited: 26
A Review of Modern Wind Power Generation Forecasting Technologies
Wen-Chang Tsai, Chih-Ming Hong, Chia‐Sheng Tu, et al.
Sustainability (2023) Vol. 15, Iss. 14, pp. 10757-10757
Open Access | Times Cited: 26
Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM
Zheng Li, Ruosi Xu, Xiaorui Luo, et al.
Energy Reports (2023) Vol. 9, pp. 6449-6460
Open Access | Times Cited: 24
Onshore and offshore wind power generation forecasting using a novel flexible time-varying fractional nonlinear grey model
Xuemei Li, Shiwei Zhou, Yufeng Zhao
Energy Conversion and Management (2023) Vol. 297, pp. 117695-117695
Closed Access | Times Cited: 24
More than accuracy: end-to-end wind power forecasting that optimises the energy system
Dariush Wahdany, Carlo Schmitt, Jochen Cremer
Electric Power Systems Research (2023) Vol. 221, pp. 109384-109384
Open Access | Times Cited: 23
Spatial correlation learning based on graph neural network for medium-term wind power forecasting
Beizhen Zhao, Xin He, Shaolin Ran, et al.
Energy (2024) Vol. 296, pp. 131164-131164
Closed Access | Times Cited: 11
Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks
Behdad Ehsani, Pierre‐Olivier Pineau, Laurent Charlin
Applied Energy (2024) Vol. 359, pp. 122649-122649
Closed Access | Times Cited: 10
Wind and Solar Power Generation Forecasting Based on Hybrid CNN-ABiLSTM, CNN-Transformer-MLP Models
Tasarruf Bashir, Huifang Wang, Mustafa Tahir, et al.
Renewable Energy (2024), pp. 122055-122055
Closed Access | Times Cited: 9
AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review
Younes Zahraoui, Tarmo Korõtko, Argo Rosin, et al.
Sustainability (2024) Vol. 16, Iss. 12, pp. 4959-4959
Open Access | Times Cited: 8
Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects
M. A. Rahman, Md. Rashidul Islam, Md. Alamgir Hossain, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108785-108785
Closed Access | Times Cited: 8
Recurrent attention encoder-decoder network for multi-step interval wind power prediction
Xiaoling Ye, Cheng‐Cheng Liu, Xiong Xiong, et al.
Energy (2025), pp. 134317-134317
Closed Access | Times Cited: 1
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