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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 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: 82

Showing 1-25 of 82 citing articles:

A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction
Jinlin Xiong, Peng Tian, Zihan Tao, et al.
Energy (2022) Vol. 266, pp. 126419-126419
Closed Access | Times Cited: 151
Short-term wind power forecasting based on Attention Mechanism and Deep Learning
Bangru Xiong, Lu Lou, Xinyu Meng, et al.
Electric Power Systems Research (2022) Vol. 206, pp. 107776-107776
Closed Access | Times Cited: 106
A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data
Zhaohua Liu, Chang-Tong Wang, Hua‐Liang Wei, et al.
Expert Systems with Applications (2024) Vol. 247, pp. 123237-123237
Closed Access | Times Cited: 18
A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction
Chao Wang, Lin Hon, Heng Hu, et al.
Energy (2024) Vol. 293, pp. 130684-130684
Closed Access | Times Cited: 16
Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting
Yanli Liu, Junyi Wang
Applied Energy (2022) Vol. 312, pp. 118729-118729
Closed Access | Times Cited: 65
Sustainable energies and machine learning: An organized review of recent applications and challenges
Pouya Ifaei, Morteza Nazari‐Heris, Amir Saman Tayerani Charmchi, et al.
Energy (2022) Vol. 266, pp. 126432-126432
Closed Access | Times Cited: 48
Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions
Jiangkuan Li, Meng Lin, Yankai Li, et al.
Energy (2022) Vol. 254, pp. 124358-124358
Closed Access | Times Cited: 47
Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy
Shaolong Sun, Zongjuan Du, Kun Jin, et al.
Applied Energy (2023) Vol. 350, pp. 121749-121749
Closed Access | Times Cited: 34
Short-term wind power prediction method based on deep clustering-improved Temporal Convolutional Network
Yiwei Sheng, Han Wang, Jie Yan, et al.
Energy Reports (2023) Vol. 9, pp. 2118-2129
Open Access | Times Cited: 30
A novel meta-learning approach for few-shot short-term wind power forecasting
Fuhao Chen, Jie Yan, Yongqian Liu, et al.
Applied Energy (2024) Vol. 362, pp. 122838-122838
Closed Access | Times Cited: 13
Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy
Xiaodi Wang, Hao Yan, Wendong Yang
Energy (2024) Vol. 297, pp. 131142-131142
Closed Access | Times Cited: 13
An instance-based transfer learning model with attention mechanism for freight train travel time prediction in the China–Europe railway express
Jingwei Guo, Wei Wang, Jiayi Guo, et al.
Expert Systems with Applications (2024) Vol. 251, pp. 123989-123989
Closed Access | Times Cited: 8
Transfer learning-based thermal error prediction and control with deep residual LSTM network
Jialan Liu, Chi Ma, Hongquan Gui, et al.
Knowledge-Based Systems (2021) Vol. 237, pp. 107704-107704
Closed Access | Times Cited: 50
Wind Power Prediction Based on Machine Learning and Deep Learning Models
Zahraa Tarek, Mahmoud Y. Shams, Ahmed M. Elshewey, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2022) Vol. 74, Iss. 1, pp. 715-732
Open Access | Times Cited: 37
A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network
Anbo Meng, Shu Chen, Zuhong Ou, et al.
Energy (2022) Vol. 261, pp. 125276-125276
Closed Access | Times Cited: 35
An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update
Ling Liu, Jujie Wang, Jianping Li, et al.
Applied Energy (2023) Vol. 340, pp. 121049-121049
Closed Access | Times Cited: 22
Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations
Mao Yang, Y. Huang, Chuanyu Xu, et al.
Applied Energy (2024) Vol. 377, pp. 124631-124631
Closed Access | Times Cited: 7
Development and trending of deep learning methods for wind power predictions
Hong Liu, Zijun Zhang
Artificial Intelligence Review (2024) Vol. 57, Iss. 5
Open Access | Times Cited: 6
Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources
Adam Krechowicz, Maria Krechowicz, Katarzyna Poczęta
Energies (2022) Vol. 15, Iss. 23, pp. 9146-9146
Open Access | Times Cited: 26
Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts
Jens Schreiber, Bernhard Sick
Energy and AI (2023) Vol. 14, pp. 100249-100249
Open Access | Times Cited: 16
A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN
Anbo Meng, Hai‐Tao Zhang, Hao Yin, et al.
Energy (2023) Vol. 283, pp. 129139-129139
Closed Access | Times Cited: 15
Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks
Jinhao Shi, Bo Wang, Kaiyi Luo, et al.
Energy (2023) Vol. 272, pp. 127116-127116
Closed 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: 14
Ensemble Transfer Learning Based Cross-Domain UAV Actuator Fault Detection
Datong Liu, Na Wang, Kai Guo, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 14, pp. 16363-16372
Closed Access | Times Cited: 13
Transfer Learning for Renewable Energy Systems: A Survey
Rami Al-Hajj, Ali Assi, Bilel Neji, et al.
Sustainability (2023) Vol. 15, Iss. 11, pp. 9131-9131
Open Access | Times Cited: 13
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