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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 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

Showing 1-25 of 178 citing articles:

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security
Lingling Lv, Zongyu Wu, Jinhua Zhang, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 9, pp. 6474-6482
Open Access | Times Cited: 176
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
SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting
Deniz Korkmaz
Applied Energy (2021) Vol. 300, pp. 117410-117410
Closed Access | Times Cited: 108
Ultra-short term wind power prediction applying a novel model named SATCN-LSTM
Ling Xiang, Jianing Liu, Xin Yang, et al.
Energy Conversion and Management (2021) Vol. 252, pp. 115036-115036
Closed Access | Times Cited: 108
Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model
Dongdong Zhang, Baian Chen, Hongyu Zhu, et al.
Energy (2023) Vol. 285, pp. 128762-128762
Closed Access | Times Cited: 71
The volatility of global energy uncertainty: Renewable alternatives
Cem Işık, Bekhzod Kuziboev, Serdar Ongan, et al.
Energy (2024) Vol. 297, pp. 131250-131250
Closed Access | Times Cited: 47
A hybrid deep learning model based on parallel architecture TCN-LSTM with Savitzky-Golay filter for wind power prediction
Shujun Liu, Tong Xu, Xiaoze Du, et al.
Energy Conversion and Management (2024) Vol. 302, pp. 118122-118122
Closed Access | Times Cited: 29
Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method
Ling Xiang, Jingxu Li, Aijun Hu, et al.
Energy Conversion and Management (2020) Vol. 220, pp. 113098-113098
Closed Access | Times Cited: 102
Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks
Dewang Chen, Jianhua Zhang, Shixiong Jiang
IEEE Access (2020) Vol. 8, pp. 91181-91187
Open Access | Times Cited: 101
State-of-the-art one-stop handbook on wind forecasting technologies: An overview of classifications, methodologies, and analysis
Bo Yang, Linen Zhong, Jingbo Wang, et al.
Journal of Cleaner Production (2020) Vol. 283, pp. 124628-124628
Closed Access | Times Cited: 88
Wind speed prediction based on singular spectrum analysis and neural network structural learning
Xiwei Mi, Shuo Zhao
Energy Conversion and Management (2020) Vol. 216, pp. 112956-112956
Closed Access | Times Cited: 82
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 novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture
Hao Yin, Zuhong Ou, Jiajin Fu, et al.
Energy (2021) Vol. 234, pp. 121271-121271
Closed Access | Times Cited: 80
Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power
Lingling Li, Zhifeng Liu, Ming‐Lang Tseng, et al.
Expert Systems with Applications (2021) Vol. 184, pp. 115579-115579
Open Access | Times Cited: 78
A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms
Yagang Zhang, Yunpeng Zhao, Xiaoyu Shen, et al.
Applied Energy (2021) Vol. 305, pp. 117815-117815
Closed Access | Times Cited: 76
A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment
Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Hatice Çıtakoğlu, et al.
Neural Computing and Applications (2021) Vol. 34, Iss. 1, pp. 783-812
Closed Access | Times Cited: 75
Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution
Yagang Zhang, Guifang Pan, Yunpeng Zhao, et al.
Energy Conversion and Management (2020) Vol. 224, pp. 113346-113346
Closed Access | Times Cited: 73
Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm
Xuefang Xu, Shiting Hu, Peiming Shi, et al.
Energy (2022) Vol. 262, pp. 125342-125342
Closed Access | Times Cited: 57
A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting
Yuyang Gao, Jianzhou Wang, Hufang Yang
Renewable Energy (2022) Vol. 188, pp. 384-401
Closed Access | Times Cited: 49
A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network
Muhammad Sibtain, Hassan Bashir, Mohsin Nawaz, et al.
Energy Conversion and Management (2022) Vol. 263, pp. 115703-115703
Closed Access | Times Cited: 39
Combined electricity load-forecasting system based on weighted fuzzy time series and deep neural networks
Zhining Cao, Jianzhou Wang, Yurui Xia
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 108375-108375
Closed Access | Times Cited: 8
Multifactor interpretability method for offshore wind power output prediction based on TPE-CatBoost-SHAP
Jia-Ling Ruan, Yun Chen, Gang Lu, et al.
Computers & Electrical Engineering (2025) Vol. 123, pp. 110081-110081
Closed Access | Times Cited: 1
An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting
Hamed H. Aly
Sustainable Energy Technologies and Assessments (2020) Vol. 41, pp. 100802-100802
Closed Access | Times Cited: 69
A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model
Bingchun Liu, Shijie Zhao, Xiaogang Yu, et al.
Energies (2020) Vol. 13, Iss. 18, pp. 4964-4964
Open Access | Times Cited: 63
Uncertain wind power forecasting using LSTM‐based prediction interval
Abhishek Banik, Chinmay Kumar Behera, Tirunagaru V. Sarathkumar, et al.
IET Renewable Power Generation (2020) Vol. 14, Iss. 14, pp. 2657-2667
Closed Access | Times Cited: 61
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