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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 Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting
Ye Ren, Ponnuthurai Nagaratnam Suganthan, Narasimalu Srikanth
IEEE Transactions on Neural Networks and Learning Systems (2014) Vol. 27, Iss. 8, pp. 1793-1798
Closed Access | Times Cited: 204

Showing 1-25 of 204 citing articles:

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
Ye Ren, Le Zhang, Ponnuthurai Nagaratnam Suganthan
IEEE Computational Intelligence Magazine (2016) Vol. 11, Iss. 1, pp. 41-53
Closed Access | Times Cited: 593
Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
Xueheng Qiu, Ye Ren, Ponnuthurai Nagaratnam Suganthan, et al.
Applied Soft Computing (2017) Vol. 54, pp. 246-255
Closed Access | Times Cited: 413
Ensemble methods for wind and solar power forecasting—A state-of-the-art review
Ye Ren, Ponnuthurai Nagaratnam Suganthan, N. V. Srikanth
Renewable and Sustainable Energy Reviews (2015) Vol. 50, pp. 82-91
Closed Access | Times Cited: 370
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
Tanveer Ahmad, Hongcai Zhang, Biao Yan
Sustainable Cities and Society (2020) Vol. 55, pp. 102052-102052
Closed Access | Times Cited: 368
A review and discussion of decomposition-based hybrid models for wind energy forecasting applications
Zheng Qian, Yan Pei, Hamidreza Zareipour, et al.
Applied Energy (2018) Vol. 235, pp. 939-953
Closed Access | Times Cited: 332
Big data analytics in smart grids: a review
Yang Zhang, Tao Huang, Ettore Bompard
Energy Informatics (2018) Vol. 1, Iss. 1
Open Access | Times Cited: 288
Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction
Ming-De Liu, Lin Ding, Yulong Bai
Energy Conversion and Management (2021) Vol. 233, pp. 113917-113917
Closed Access | Times Cited: 263
Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression
Zhendong Zhang, Lei Ye, Hui Qin, et al.
Applied Energy (2019) Vol. 247, pp. 270-284
Closed Access | Times Cited: 260
Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction
Santhosh Madasthu, Chintham Venkaiah, D. M. Vinod Kumar
Energy Conversion and Management (2018) Vol. 168, pp. 482-493
Closed Access | Times Cited: 189
A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic
Yagang Zhang, Yuan Zhao, Chunhui Kong, et al.
Energy Conversion and Management (2019) Vol. 203, pp. 112254-112254
Closed Access | Times Cited: 178
Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression
Jyotirmayee Naik, Prachitara Satapathy, P.K. Dash
Applied Soft Computing (2017) Vol. 70, pp. 1167-1188
Closed Access | Times Cited: 177
A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction
Neeraj Dhanraj Bokde, Andrés Feijóo, Daniel Villanueva, et al.
Energies (2019) Vol. 12, Iss. 2, pp. 254-254
Open Access | Times Cited: 150
Evolutionary Dynamic Multiobjective Optimization Assisted by a Support Vector Regression Predictor
Leilei Cao, Lihong Xu, Erik D. Goodman, et al.
IEEE Transactions on Evolutionary Computation (2019) Vol. 24, Iss. 2, pp. 305-319
Open Access | Times Cited: 145
Load Forecasting Techniques for Power System: Research Challenges and Survey
Naqash Ahmad, Yazeed Yasin Ghadi, Muhammad Adnan, et al.
IEEE Access (2022) Vol. 10, pp. 71054-71090
Open Access | Times Cited: 133
Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
Vijaya Krishna Rayi, Sthita Prajna Mishra, Jyotirmayee Naik, et al.
Energy (2021) Vol. 244, pp. 122585-122585
Closed Access | Times Cited: 104
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Ruobin Gao, Liang Du, Ponnuthurai Nagaratnam Suganthan, et al.
Expert Systems with Applications (2022) Vol. 206, pp. 117784-117784
Open Access | Times Cited: 72
Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine
Peng Tian, Jianzhong Zhou, Chu Zhang, et al.
Energy Conversion and Management (2017) Vol. 153, pp. 589-602
Closed Access | Times Cited: 145
Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting
Yusuf Yaslan, Bahadır Bican
Measurement (2017) Vol. 103, pp. 52-61
Closed Access | Times Cited: 143
Wind power forecast based on improved Long Short Term Memory network
Li Han, Huitian Jing, Rongchang Zhang, et al.
Energy (2019) Vol. 189, pp. 116300-116300
Closed Access | Times Cited: 133
Wind speed and wind direction forecasting using echo state network with nonlinear functions
Mohammad Amin Chitsazan, M. Sami Fadali, A.M. Trzynadlowski
Renewable Energy (2018) Vol. 131, pp. 879-889
Closed Access | Times Cited: 124
Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction
Hui Liu, Haiping Wu, Yanfei Li
Energy Conversion and Management (2018) Vol. 161, pp. 266-283
Closed Access | Times Cited: 109
A review of applications of artificial intelligent algorithms in wind farms
Yirui Wang, Yang Yu, Shuyang Cao, et al.
Artificial Intelligence Review (2019) Vol. 53, Iss. 5, pp. 3447-3500
Closed Access | Times Cited: 106
Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction
Hui Liu, Chao Chen
Applied Energy (2019) Vol. 254, pp. 113686-113686
Closed Access | Times Cited: 100
A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM
Wenlong Fu, Kai Zhang, Kai Wang, et al.
Renewable Energy (2020) Vol. 164, pp. 211-229
Closed Access | Times Cited: 86
Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks
Fei Li, Guorui Ren, Jay Lee
Energy Conversion and Management (2019) Vol. 186, pp. 306-322
Closed Access | Times Cited: 78
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