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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:

Short-Term Wind Speed Forecasting Based on Information of Neighboring Wind Farms
Zhongju Wang, Jing Zhang, Zhang Yu, et al.
IEEE Access (2020) Vol. 8, pp. 16760-16770
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Multistep short-term wind speed forecasting using transformer
Huijuan Wu, Keqilao Meng, Daoerji Fan, et al.
Energy (2022) Vol. 261, pp. 125231-125231
Open Access | Times Cited: 84
Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network
Ying‐Yi Hong, Thursy Rienda Aulia Satriani
Energy (2020) Vol. 209, pp. 118441-118441
Closed Access | Times Cited: 93
A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture
Hao Yin, Zuhong Ou, Jiajin Fu, et al.
Energy (2021) Vol. 234, pp. 121271-121271
Closed Access | Times Cited: 79
Short-term wind speed forecasting based on spatial-temporal graph transformer networks
Xiaoxin Pan, Long Wang, Zhongju Wang, et al.
Energy (2022) Vol. 253, pp. 124095-124095
Closed Access | Times Cited: 51
Short-Term Wind Speed and Power Forecasting for Smart City Power Grid With a Hybrid Machine Learning Framework
Zhongju Wang, Long Wang, M. Revanesh, et al.
IEEE Internet of Things Journal (2023) Vol. 10, Iss. 21, pp. 18754-18765
Closed Access | Times Cited: 28
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
Wind Speed Forecasting Based on Extreme Gradient Boosting
Cai Ren, Sen Xie, Bozhong Wang, et al.
IEEE Access (2020) Vol. 8, pp. 175063-175069
Open Access | Times Cited: 52
A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting
Jie Liu, Quan Shi, Ruilian Han, et al.
Energies (2021) Vol. 14, Iss. 20, pp. 6500-6500
Open Access | Times Cited: 41
A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia
Ejigu Tefera Habtemariam, Kula Kekeba, M. Martínez-Ballesteros, et al.
Energies (2023) Vol. 16, Iss. 5, pp. 2317-2317
Open Access | Times Cited: 18
Hybrid deep learning and quantum-inspired neural network for day-ahead spatiotemporal wind speed forecasting
Ying‐Yi Hong, Christian Lian Paulo P. Rioflorido, Weina Zhang
Expert Systems with Applications (2023) Vol. 241, pp. 122645-122645
Closed Access | Times Cited: 17
Advancements in wind power forecasting: A comprehensive review of artificial intelligence-based approaches
Krishan Kumar, Priti Prabhakar, Avnesh Verma, et al.
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 5
An Improved Sunflower Optimization Algorithm-Based Monte Carlo Simulation for Efficiency Improvement of Radial Distribution Systems Considering Wind Power Uncertainty
Abdullah M. Shaheen, Ehab E. Elattar, Ragab A. El‐Sehiemy, et al.
IEEE Access (2020) Vol. 9, pp. 2332-2344
Open Access | Times Cited: 48
A Deep Convolutional Neural Network Model for Improving WRF Simulations
Alqamah Sayeed, Yunsoo Choi, Jia Jung, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 34, Iss. 2, pp. 750-760
Open Access | Times Cited: 39
Deep Spatial-Temporal 2-D CNN-BLSTM Model for Ultrashort-Term LiDAR-Assisted Wind Turbine's Power and Fatigue Load Forecasting
Amirhossein Dolatabadi, Hussein Abdeltawab, Yasser Abdel‐Rady I. Mohamed
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 4, pp. 2342-2353
Closed Access | Times Cited: 37
Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms
P. Piotrowski, D. Baczyński, Marcin Kopyt, et al.
Energies (2022) Vol. 15, Iss. 4, pp. 1252-1252
Open Access | Times Cited: 27
A dagging‐based deep learning framework for transmission line flexibility assessment
Azita Morteza, Masod Sadipour, Reza Saadati Fard, et al.
IET Renewable Power Generation (2022) Vol. 17, Iss. 5, pp. 1092-1105
Open Access | Times Cited: 23
Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors
P. Piotrowski, Inajara Rutyna, D. Baczyński, et al.
Energies (2022) Vol. 15, Iss. 24, pp. 9657-9657
Open Access | Times Cited: 23
Cooperative evaluation of multiple wind farms based on information in neighboring wind farms and improved TCN
Long Cheng, Li Xu, Lihua Li, et al.
Wind Engineering (2025)
Closed Access
Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model
Yanlong Gao, Feng Xing, L. Kang, et al.
IEEE Access (2025) Vol. 13, pp. 22919-22930
Open Access
Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms
Jiaxiang Hu, Weihao Hu, Di Cao, et al.
Applied Energy (2023) Vol. 355, pp. 122185-122185
Closed Access | Times Cited: 11
An Ensemble Neural Network Based on Variational Mode Decomposition and an Improved Sparrow Search Algorithm for Wind and Solar Power Forecasting
Zhiqiang Wu, Bo Wang
IEEE Access (2021) Vol. 9, pp. 166709-166719
Open Access | Times Cited: 26
On wavelet transform based convolutional neural network and twin support vector regression for wind power ramp event prediction
Harsh S. Dhiman, Dipankar Deb, Josep M. Guerrero
Sustainable Computing Informatics and Systems (2022) Vol. 36, pp. 100795-100795
Open Access | Times Cited: 18
The Short-Term Prediction of Wind Power Based on the Convolutional Graph Attention Deep Neural Network
Fan Xiao, Xiong Ping, Y. Li, et al.
Energy Engineering (2024) Vol. 121, Iss. 2, pp. 359-376
Open Access | Times Cited: 3
Improving the accuracy of flood forecasting for Northeast China by the correction of global forecast rainfall based on deep learning
Songliang Chen, Youcan Feng, Qinglin Mao, et al.
Journal of Hydrology (2024) Vol. 640, pp. 131733-131733
Closed Access | Times Cited: 3
Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions
Fhulufhelo Walter Mugware, Caston Sigauke, Thakhani Ravele
Forecasting (2024) Vol. 6, Iss. 3, pp. 672-699
Open Access | Times Cited: 3
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