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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 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

Showing 1-25 of 145 citing articles:

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Sadeq Oleiwi Sulaiman, Ravinesh C. Deo, et al.
Journal of Hydrology (2018) Vol. 569, pp. 387-408
Closed Access | Times Cited: 623
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
A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
Aytaç Altan, Seçkin Karasu, Enrico Zio
Applied Soft Computing (2020) Vol. 100, pp. 106996-106996
Open Access | Times Cited: 514
Wind power forecasting based on daily wind speed data using machine learning algorithms
Halil Demolli, Ahmet Şakir Dokuz, Alper Ecemiş, et al.
Energy Conversion and Management (2019) Vol. 198, pp. 111823-111823
Closed Access | Times Cited: 352
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
Data processing strategies in wind energy forecasting models and applications: A comprehensive review
Hui Liu, Chao Chen
Applied Energy (2019) Vol. 249, pp. 392-408
Closed Access | Times Cited: 296
A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm
Feifei He, Jianzhong Zhou, Zhong-kai Feng, et al.
Applied Energy (2019) Vol. 237, pp. 103-116
Closed Access | Times Cited: 287
Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network
Hui Liu, Xiwei Mi, Yanfei Li
Energy Conversion and Management (2018) Vol. 166, pp. 120-131
Closed Access | Times Cited: 286
A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm
Ya-Lan Hu, Liang Chen
Energy Conversion and Management (2018) Vol. 173, pp. 123-142
Closed Access | Times Cited: 284
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
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
An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting
Peng Tian, Chu Zhang, Jianzhong Zhou, et al.
Energy (2021) Vol. 221, pp. 119887-119887
Closed Access | Times Cited: 244
A novel hybrid system based on multi-objective optimization for wind speed forecasting
Chunying Wu, Jianzhou Wang, Xuejun Chen, et al.
Renewable Energy (2019) Vol. 146, pp. 149-165
Closed Access | Times Cited: 185
A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, et al.
Energy Conversion and Management (2021) Vol. 236, pp. 114002-114002
Closed Access | Times Cited: 179
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
Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM
Wenlong Fu, Kai Wang, Chaoshun Li, et al.
Energy Conversion and Management (2019) Vol. 187, pp. 356-377
Closed Access | Times Cited: 162
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
Upma Singh, M. Rizwan, Muhannad Alaraj, et al.
Energies (2021) Vol. 14, Iss. 16, pp. 5196-5196
Open Access | Times Cited: 134
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
Peng Lu, Lin Ye, Yongning Zhao, et al.
Applied Energy (2021) Vol. 301, pp. 117446-117446
Closed Access | Times Cited: 131
Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction
Lei Hua, Chu Zhang, Peng Tian, et al.
Energy Conversion and Management (2021) Vol. 252, pp. 115102-115102
Closed Access | Times Cited: 125
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
Deep learning-based multistep ahead wind speed and power generation forecasting using direct method
Maryam Yaghoubirad, Narjes Azizi, Meisam Farajollahi, et al.
Energy Conversion and Management (2023) Vol. 281, pp. 116760-116760
Closed Access | Times Cited: 50
Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model
Zhipeng Gong, Anping Wan, Yunsong Ji, et al.
Energy (2024) Vol. 295, pp. 131016-131016
Closed Access | Times Cited: 15
Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction
Yanfei Li, Haiping Wu, Hui Liu
Energy Conversion and Management (2018) Vol. 167, pp. 203-219
Closed Access | Times Cited: 159
Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters
Elham Fijani, Rahim Barzegar, Ravinesh C. Deo, et al.
The Science of The Total Environment (2018) Vol. 648, pp. 839-853
Closed Access | Times Cited: 157
An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm
Hui Liu, Xiwei Mi, Yanfei Li
Renewable Energy (2018) Vol. 123, pp. 694-705
Closed Access | Times Cited: 149
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