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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 predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning
Xin Liu, Zheming Cao, Zijun Zhang
Energy (2020) Vol. 217, pp. 119356-119356
Closed Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

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
A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution
Adnan Saeed, Chaoshun Li, Zhenhao Gan, et al.
Energy (2021) Vol. 238, pp. 122012-122012
Closed Access | Times Cited: 62
Short-term wind power interval prediction method using VMD-RFG and Att-GRU
Hongyi Liu, Hua Han, Yao Sun, et al.
Energy (2022) Vol. 251, pp. 123807-123807
Closed Access | Times Cited: 60
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
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: 42
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: 25
A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects
Hong Qiu, Kaikai Shi, Renfang Wang, et al.
Renewable Energy (2024) Vol. 227, pp. 120499-120499
Closed Access | Times Cited: 13
Temporal collaborative attention for wind power forecasting
Yue Hu, Hanjing Liu, Senzhen Wu, et al.
Applied Energy (2023) Vol. 357, pp. 122502-122502
Open Access | Times Cited: 17
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
VMD-SEAE-TL-Based Data-Driven soft sensor modeling for a complex industrial batch processes
Jun-Chao Ren, Ding Liu, Yin Wan
Measurement (2022) Vol. 198, pp. 111439-111439
Closed Access | Times Cited: 24
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
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
A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting
Yugui Tang, Shujing Zhang, Zhen Zhang
Energy (2023) Vol. 286, pp. 129639-129639
Closed Access | Times Cited: 14
Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network
Qiuwan Du, Like Yang, Liangliang Li, et al.
Energy (2021) Vol. 244, pp. 122617-122617
Closed Access | Times Cited: 27
A Three-stage Adjustable Robust Optimization Framework for Energy Base Leveraging Transfer Learning
Yuan Gao, Yucan Zhao, Sile Hu, et al.
Energy (2025), pp. 135037-135037
Closed Access
Knowledge Distillation with Error-Correcting Transfer Learning for Wind Power Prediction
Hao Chen
(2025)
Closed Access
Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
Zachary Barbre, Gang Li
Algorithms (2025) Vol. 18, Iss. 4, pp. 206-206
Open Access
Wake and output power prediction across different types of offshore wind turbines using transfer learning and meta-learning
Songyue Liu, Q.S. Li
Physics of Fluids (2025) Vol. 37, Iss. 3
Closed Access
A multi-layer extreme learning machine refined by sparrow search algorithm and weighted mean filter for short-term multi-step wind speed forecasting
Haochen Zhang, Zhiyun Peng, Junjie Tang, et al.
Sustainable Energy Technologies and Assessments (2021) Vol. 50, pp. 101698-101698
Closed Access | Times Cited: 25
Wind process pattern forecasting based ultra-short-term wind speed hybrid prediction
Fei Wang, Shuang Tong, Yiqian Sun, et al.
Energy (2022) Vol. 255, pp. 124509-124509
Closed Access | Times Cited: 18
Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training
Yugui Tang, Kuo Yang, Yichu Zheng, et al.
Renewable Energy (2024) Vol. 224, pp. 120200-120200
Closed Access | Times Cited: 3
Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction
Yingqin Zhu, Yue Liu, Nan Wang, et al.
Applied Energy (2024) Vol. 379, pp. 124893-124893
Open Access | Times Cited: 3
Transferable wind power probabilistic forecasting based on multi-domain adversarial networks
Xiaochong Dong, Yingyun Sun, Lei Dong, et al.
Energy (2023) Vol. 285, pp. 129496-129496
Closed Access | Times Cited: 8
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