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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:

An overview of deterministic and probabilistic forecasting methods of wind energy
Yuying Xie, Chaoshun Li, Mengying Li, et al.
iScience (2022) Vol. 26, Iss. 1, pp. 105804-105804
Open Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning
Lucas de Azevedo Takara, Ana Clara Teixeira, Hamed Yazdanpanah, et al.
Applied Energy (2024) Vol. 369, pp. 123487-123487
Closed Access | Times Cited: 17
A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies
Владимир Сергеевич Симанков, Pavel Yu. Buchatskiy, Anatoliy Kazak, et al.
Energies (2024) Vol. 17, Iss. 2, pp. 416-416
Open Access | Times Cited: 11
A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation
Guolian Hou, Junjie Wang, Yuzhen Fan, et al.
Renewable Energy (2024) Vol. 226, pp. 120360-120360
Closed Access | Times Cited: 11
Designing and prototyping the architecture of a digital twin for wind turbine
Montaser Mahmoud, Concetta Semeraro, Mohammad Ali Abdelkareem, et al.
International Journal of Thermofluids (2024) Vol. 22, pp. 100622-100622
Open Access | Times Cited: 7
Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting
Jing‐yao Liu, Jiajia Chen, Guijin Yan, et al.
iScience (2023) Vol. 26, Iss. 8, pp. 107456-107456
Open Access | Times Cited: 18
Age estimation through sternal fusion and costal cartilage ossification using MSCT in a Croatian population: model development and application
Josip Vickov, Ivan Jerković, Iva Perić, et al.
International Journal of Legal Medicine (2025)
Closed Access
A machine learning approach for wind turbine power forecasting for maintenance planning
Hariom Dhungana
Energy Informatics (2025) Vol. 8, Iss. 1
Open Access
Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting
Álvaro Abad‐Santjago, C. Peláez‐Rodríguez, Jorge Pérez‐Aracil, et al.
Expert Systems (2025) Vol. 42, Iss. 2
Open Access
Probabilistic wind speed forecasting via Bayesian DLMs and its application in green hydrogen production
J. Leal, Anselmo Ramalho Pitombeira Neto, André Valente Bueno, et al.
Applied Energy (2025) Vol. 382, pp. 125286-125286
Closed Access
Development of wind energy and solar energy
I. Turduev, N. Abdiraeva, V. Mamatova, et al.
E3S Web of Conferences (2025) Vol. 614, pp. 01007-01007
Open Access
Privacy-Protected Short-Term Wind Power Prediction Based on Vertical Federated Learning
Xijin Guo, Yujian Ye, Jianxiong Hu, et al.
Lecture notes in electrical engineering (2025), pp. 474-492
Closed Access
Deep learning methods and evaluation of the extensive carbon emission predictive solution for Danish grid
Seyed Mahdi Miraftabzadeh, Mohammed Ali Khan, Navid Bayati, et al.
Sustainable Energy Technologies and Assessments (2025) Vol. 75, pp. 104242-104242
Open Access
Leveraging state‐of‐the‐art AI models to forecast wind power generation using deep learning
Lew Hardy, Isla Finney
Meteorological Applications (2025) Vol. 32, Iss. 2
Open Access
Review of Estimating and Predicting Models of the Wind Energy Amount
Владимир Сергеевич Симанков, Pavel Yu. Buchatskiy, Semen V. Teploukhov, et al.
Energies (2023) Vol. 16, Iss. 16, pp. 5926-5926
Open Access | Times Cited: 14
DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting
Mingwei Zhong, Cancheng Xu, Zikang Xian, et al.
Energy (2023) Vol. 286, pp. 129588-129588
Closed Access | Times Cited: 12
Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods
Rita Teixeira, Adelaide Cerveira, E. J. Solteiro Pires, et al.
Energies (2024) Vol. 17, Iss. 14, pp. 3480-3480
Open Access | Times Cited: 4
Short-term probabilistic load forecasting method based on uncertainty estimation and deep learning model considering meteorological factors
Bin Li, Y. J. Mo, Feng Gao, et al.
Electric Power Systems Research (2023) Vol. 225, pp. 109804-109804
Closed Access | Times Cited: 10
Machine learning and artificial intelligence-distributed renewable energy sources: technologies, perspectives, and challenges
Xiaojun Yu, Yuekuan Zhou
Elsevier eBooks (2024), pp. 17-30
Closed Access | Times Cited: 3
Developing a housing stock model for evaluating energy Performance: The case of Jordan
Reham Alasmar, Yair Schwartz, Esfand Burman
Energy and Buildings (2024) Vol. 308, pp. 114010-114010
Open Access | Times Cited: 3
Stochastic optimisation of district integrated energy systems based on a hybrid probability forecasting model
Yi Yan, Xuerui Wang, Ke Li, et al.
Energy (2024) Vol. 306, pp. 132486-132486
Closed Access | Times Cited: 3
Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation
Yuejiang Chen, Jiang‐Wen Xiao, Yan‐Wu Wang, et al.
Applied Energy (2024) Vol. 377, pp. 124356-124356
Closed Access | Times Cited: 3
Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach
Antonella R. Finamore, Vito Calderaro, Vincenzo Galdi, et al.
Energies (2023) Vol. 16, Iss. 22, pp. 7522-7522
Open Access | Times Cited: 8
Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration
Roberta Baggio, J. F. Muzy
Applied Energy (2024) Vol. 360, pp. 122840-122840
Open Access | Times Cited: 2
Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach
Simon Daenens, Ivo Vervlimmeren, Timothy Verstraeten, et al.
Journal of Physics Conference Series (2024) Vol. 2767, Iss. 9, pp. 092014-092014
Open Access | Times Cited: 1
Attack-resilient framework for wind power forecasting against civil and adversarial attacks
Khadija Akter, M. A. Rahman, Md. Rashidul Islam, et al.
Electric Power Systems Research (2024) Vol. 238, pp. 111065-111065
Closed Access | Times Cited: 1
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