Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Wind power prediction using deep neural network based meta regression and transfer learning
Aqsa Saeed Qureshi, Asifullah Khan, Aneela Zameer, et al.
Applied Soft Computing (2017) Vol. 58, pp. 742-755
Closed Access | Times Cited: 342

Showing 1-25 of 342 citing articles:

A survey of the recent architectures of deep convolutional neural networks
Asifullah Khan, Anabia Sohail, Umme Zahoora, et al.
Artificial Intelligence Review (2020) Vol. 53, Iss. 8, pp. 5455-5516
Closed Access | Times Cited: 2441
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Razin Ahmed, Victor Sreeram, Yateendra Mishra, et al.
Renewable and Sustainable Energy Reviews (2020) Vol. 124, pp. 109792-109792
Closed Access | Times Cited: 834
A review of wind speed and wind power forecasting with deep neural networks
Yun Wang, Runmin Zou, Fang Liu, et al.
Applied Energy (2021) Vol. 304, pp. 117766-117766
Closed Access | Times Cited: 545
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application
Te Han, Chao Liu, Wenguang Yang, et al.
ISA Transactions (2019) Vol. 97, pp. 269-281
Open Access | Times Cited: 473
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
Matheus Henrique Dal Molin Ribeiro, Leandro dos Santos Coelho
Applied Soft Computing (2019) Vol. 86, pp. 105837-105837
Closed Access | Times Cited: 428
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
Lin Li, Xue Zhao, Ming‐Lang Tseng, et al.
Journal of Cleaner Production (2019) Vol. 242, pp. 118447-118447
Closed Access | Times Cited: 363
A novel genetic LSTM model for wind power forecast
Farah Shahid, Aneela Zameer, Muhammad Muneeb
Energy (2021) Vol. 223, pp. 120069-120069
Closed Access | Times Cited: 346
A review of deep learning with special emphasis on architectures, applications and recent trends
Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, et al.
Knowledge-Based Systems (2020) Vol. 194, pp. 105596-105596
Open Access | Times Cited: 335
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-28
Open Access | Times Cited: 329
Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
Haidong Shao, Hongkai Jiang, Xingqiu Li, et al.
Knowledge-Based Systems (2017) Vol. 140, pp. 1-14
Closed Access | Times Cited: 291
A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources
Shahab S. Band, Timon Rabczuk, Kwok‐wing Chau
IEEE Access (2019) Vol. 7, pp. 164650-164666
Open Access | Times Cited: 276
Deep belief network based k-means cluster approach for short-term wind power forecasting
Kejun Wang, Xiaoxia Qi, Hongda Liu, et al.
Energy (2018) Vol. 165, pp. 840-852
Closed Access | Times Cited: 265
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Hui Liu, Chao Chen, Xinwei Lv, et al.
Energy Conversion and Management (2019) Vol. 195, pp. 328-345
Closed Access | Times Cited: 250
Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM
Mingming Gao, Jianjing Li, Feng Hong, et al.
Energy (2019) Vol. 187, pp. 115838-115838
Closed Access | Times Cited: 237
Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning
Haixiang Zang, Lilin Cheng, Tao Ding, et al.
International Journal of Electrical Power & Energy Systems (2019) Vol. 118, pp. 105790-105790
Closed Access | Times Cited: 222
Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine
Xiwei Mi, Hui Liu, Yanfei Li
Energy Conversion and Management (2018) Vol. 180, pp. 196-205
Closed Access | Times Cited: 221
Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network
Haixiang Zang, Lilin Cheng, Tao Ding, et al.
IET Generation Transmission & Distribution (2018) Vol. 12, Iss. 20, pp. 4557-4567
Closed Access | Times Cited: 210
A review and taxonomy of wind and solar energy forecasting methods based on deep learning
Ghadah Alkhayat, Rashid Mehmood
Energy and AI (2021) Vol. 4, pp. 100060-100060
Open Access | Times Cited: 204
A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
Yuzhen Zhang, Jingjing Liu, Wenjuan Shen
Applied Sciences (2022) Vol. 12, Iss. 17, pp. 8654-8654
Open Access | Times Cited: 180
Deep learning methods and applications for electrical power systems: A comprehensive review
Asiye Kaymaz Özcanlı, Fatma Yaprakdal, Mustafa Baysal
International Journal of Energy Research (2020) Vol. 44, Iss. 9, pp. 7136-7157
Open Access | Times Cited: 179
Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system
Han Wang, Shuang Han, Yongqian Liu, et al.
Applied Energy (2019) Vol. 237, pp. 1-10
Closed Access | Times Cited: 176
Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting
Yong Chen, Shuai Zhang, Wenyu Zhang, et al.
Energy Conversion and Management (2019) Vol. 185, pp. 783-799
Closed Access | Times Cited: 175
Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression
Hui Liu, Xiwei Mi, Yanfei Li, et al.
Renewable Energy (2019) Vol. 143, pp. 842-854
Closed Access | Times Cited: 174
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
Daniel Rangel-Martinez, K.D.P. Nigam, Luis Ricardez‐Sandoval
Process Safety and Environmental Protection (2021) Vol. 174, pp. 414-441
Closed Access | Times Cited: 172
Prediction interval of wind power using parameter optimized Beta distribution based LSTM model
Xiaohui Yuan, Chen Chen, Min Jiang, et al.
Applied Soft Computing (2019) Vol. 82, pp. 105550-105550
Closed Access | Times Cited: 168
Page 1 - Next Page

Scroll to top