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:

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

Showing 26-50 of 172 citing articles:

Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy
Jujie Wang, Yaning Li
Applied Energy (2018) Vol. 230, pp. 429-443
Closed Access | Times Cited: 129
Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks
Hui Liu, Xiwei Mi, Yanfei Li
Energy Conversion and Management (2017) Vol. 155, pp. 188-200
Closed Access | Times Cited: 128
Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition
Xiaoan Yan, Ying Liu, Yadong Xu, et al.
Energy Conversion and Management (2020) Vol. 225, pp. 113456-113456
Closed Access | Times Cited: 114
On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation
Yamin Wang, Lei Wu
Energy (2016) Vol. 112, pp. 208-220
Open Access | Times Cited: 110
Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm
Lingling Li, Yun-Biao Chang, Ming‐Lang Tseng, et al.
Journal of Cleaner Production (2020) Vol. 270, pp. 121817-121817
Closed Access | Times Cited: 108
Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction
Yan Jiang, Guoqing Huang
Energy Conversion and Management (2017) Vol. 144, pp. 340-350
Closed Access | Times Cited: 106
Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism
Shuai Zhang, Yong Chen, Jiuhong Xiao, et al.
Renewable Energy (2021) Vol. 174, pp. 688-704
Closed Access | Times Cited: 96
Short-term wind speed forecasting approach using Ensemble Empirical Mode Decomposition and Deep Boltzmann Machine
Santhosh Madasthu, Chintham Venkaiah, D. M. Vinod Kumar
Sustainable Energy Grids and Networks (2019) Vol. 19, pp. 100242-100242
Closed Access | Times Cited: 92
Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions
Haiyan Jiang, Jianzhou Wang, Jie Wu, et al.
Renewable and Sustainable Energy Reviews (2016) Vol. 69, pp. 1199-1217
Closed Access | Times Cited: 90
A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition
Hao Yin, Zuhong Ou, Shengquan Huang, et al.
Energy (2019) Vol. 189, pp. 116316-116316
Closed Access | Times Cited: 90
An adaptive hybrid model for short term wind speed forecasting
Jinliang Zhang, Yi‐Ming Wei, Zhongfu Tan
Energy (2019) Vol. 190, pp. 115615-115615
Closed Access | Times Cited: 86
Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting
Zhuoyi Liu, Ryoichi Hara, Hiroyuki Kita
Energy Conversion and Management (2021) Vol. 238, pp. 114136-114136
Open Access | Times Cited: 86
A multi-stage predicting methodology based on data decomposition and error correction for ultra-short-term wind energy prediction
Yagang Zhang, Jingyi Han, Guifang Pan, et al.
Journal of Cleaner Production (2021) Vol. 292, pp. 125981-125981
Closed Access | Times Cited: 82
A permutation entropy-based EMD–ANN forecasting ensemble approach for wind speed prediction
Juan Jesús Ruíz-Aguilar, Ignacio J. Turias, Javier González-Enrique, et al.
Neural Computing and Applications (2020) Vol. 33, Iss. 7, pp. 2369-2391
Closed Access | Times Cited: 70
A novel loss function of deep learning in wind speed forecasting
Xi Chen, Ruyi Yu, Sajid Ullah, et al.
Energy (2021) Vol. 238, pp. 121808-121808
Closed Access | Times Cited: 65
A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm
Mingwei Li, Dongyang Xu, Jing Geng, et al.
Nonlinear Dynamics (2022) Vol. 107, Iss. 3, pp. 2447-2467
Closed Access | Times Cited: 65
Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)
Cem Emeksiz, Mustafa Tan
Energy (2022) Vol. 249, pp. 123785-123785
Closed Access | Times Cited: 44
Wind power forecasting based on new hybrid model with TCN residual modification
Jiaojiao Zhu, Liancheng Su, Yingwei Li
Energy and AI (2022) Vol. 10, pp. 100199-100199
Open Access | Times Cited: 43
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
Short-term wind power forecast based on chaotic analysis and multivariate phase space reconstruction
Tianyao Ji, Jin Wang, Mengshi Li, et al.
Energy Conversion and Management (2022) Vol. 254, pp. 115196-115196
Closed Access | Times Cited: 41
Artificial Intelligence in Wind Speed Forecasting: A Review
Sandra Minerva Valdivia-Bautista, José A. Domínguez‐Navarro, Marco Pérez‐Cisneros, et al.
Energies (2023) Vol. 16, Iss. 5, pp. 2457-2457
Open Access | Times Cited: 37
Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model
Lang Zheng, Weisheng Lu, Qianyun Zhou
Building and Environment (2023) Vol. 239, pp. 110446-110446
Closed Access | Times Cited: 25
Wind speed short-term prediction using recurrent neural network GRU model and stationary wavelet transform GRU hybrid model
Darío Gerardo Fantini, Reginaldo Nunes da Silva, Mario Siqueira, et al.
Energy Conversion and Management (2024) Vol. 308, pp. 118333-118333
Closed Access | Times Cited: 13
A novel wind speed prediction method: Hybrid of correlation-aided DWT, LSSVM and GARCH
Yan Jiang, Guoqing Huang, Xinyan Peng, et al.
Journal of Wind Engineering and Industrial Aerodynamics (2017) Vol. 174, pp. 28-38
Closed Access | Times Cited: 78
Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach
Weiqin Zheng, Xiangang Peng, Di Lu, et al.
Energy Conversion and Management (2017) Vol. 151, pp. 737-752
Closed Access | Times Cited: 75

Scroll to top