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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:

Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
Zhenhai Guo, Weigang Zhao, Haiyan Lu, et al.
Renewable Energy (2011) Vol. 37, Iss. 1, pp. 241-249
Closed Access | Times Cited: 501

Showing 26-50 of 501 citing articles:

A novel hybrid model for short-term wind power forecasting
Pei Du, Jianzhou Wang, Wendong Yang, et al.
Applied Soft Computing (2019) Vol. 80, pp. 93-106
Closed Access | Times Cited: 236
Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
Jianzhou Wang, Yiliao Song, Feng Liu, et al.
Renewable and Sustainable Energy Reviews (2016) Vol. 60, pp. 960-981
Closed Access | Times Cited: 235
Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition
Wenchuan Wang, Kwok‐wing Chau, Lin Qiu, et al.
Environmental Research (2015) Vol. 139, pp. 46-54
Closed Access | Times Cited: 221
A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network
Wei Sun, Chenchen Huang
Journal of Cleaner Production (2019) Vol. 243, pp. 118671-118671
Closed Access | Times Cited: 221
A hybrid forecasting approach applied to wind speed time series
Jianming Hu, Jianzhou Wang, Guowei Zeng
Renewable Energy (2013) Vol. 60, pp. 185-194
Closed Access | Times Cited: 217
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
Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression
Jianming Hu, Jianzhou Wang
Energy (2015) Vol. 93, pp. 1456-1466
Closed Access | Times Cited: 200
A novel bidirectional mechanism based on time series model for wind power forecasting
Yongning Zhao, Lin Ye, Zhi Li, et al.
Applied Energy (2016) Vol. 177, pp. 793-803
Open Access | Times Cited: 198
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
Combined forecasting models for wind energy forecasting: A case study in China
Ling Xiao, Jianzhou Wang, Yao Dong, et al.
Renewable and Sustainable Energy Reviews (2015) Vol. 44, pp. 271-288
Closed Access | Times Cited: 180
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
Short-term wind speed forecasting using empirical mode decomposition and feature selection
Chi Zhang, Haikun Wei, Junsheng Zhao, et al.
Renewable Energy (2016) Vol. 96, pp. 727-737
Closed Access | Times Cited: 172
Wind speed forecasting based on variational mode decomposition and improved echo state network
Huanling Hu, Lin Wang, Rui Tao
Renewable Energy (2020) Vol. 164, pp. 729-751
Closed Access | Times Cited: 162
Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy
Weifeng Xu, Pan Liu, Lei Cheng, et al.
Renewable Energy (2020) Vol. 163, pp. 772-782
Closed Access | Times Cited: 161
Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects
Amani Al–Othman, Muhammad Tawalbeh, Remston Martis, et al.
Energy Conversion and Management (2021) Vol. 253, pp. 115154-115154
Closed Access | Times Cited: 160
A review on multi-objective optimization framework in wind energy forecasting techniques and applications
Hui Liu, Ye Li, Zhu Duan, et al.
Energy Conversion and Management (2020) Vol. 224, pp. 113324-113324
Closed Access | Times Cited: 156
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
Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks
Dan Li, Fuxin Jiang, Min Chen, et al.
Energy (2021) Vol. 238, pp. 121981-121981
Closed Access | Times Cited: 135
A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting
Yan Han, Lihua Mi, Lian Shen, et al.
Applied Energy (2022) Vol. 312, pp. 118777-118777
Closed Access | Times Cited: 114
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
Bridge deformation prediction based on SHM data using improved VMD and conditional KDE
Jingzhou Xin, Yan Jiang, Jianting Zhou, et al.
Engineering Structures (2022) Vol. 261, pp. 114285-114285
Closed Access | Times Cited: 89
A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network
Ziyuan Zhang, Jianzhou Wang, Danxiang Wei, et al.
Renewable Energy (2023) Vol. 204, pp. 11-23
Closed Access | Times Cited: 58
Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S
Xiaoying Sun, Haizhong Liu
Energy (2024) Vol. 305, pp. 132228-132228
Closed Access | Times Cited: 19
Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting
Ning An, Weigang Zhao, Jianzhou Wang, et al.
Energy (2012) Vol. 49, pp. 279-288
Closed Access | Times Cited: 186

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