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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:

Short-Term Power Load Forecasting Based on a Combination of VMD and ELM
Wei Li, Congxin Quan, Xuyang Wang, et al.
Polish Journal of Environmental Studies (2018) Vol. 27, Iss. 5, pp. 2143-2154
Open Access | Times Cited: 22

Showing 22 citing articles:

A novel composite electricity demand forecasting framework by data processing and optimized support vector machine
Ping Jiang, Ranran Li, Ningning Liu, et al.
Applied Energy (2020) Vol. 260, pp. 114243-114243
Closed Access | Times Cited: 114
Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization
Dongxiao Niu, Zhengsen Ji, Wanying Li, et al.
Energy (2021) Vol. 234, pp. 121145-121145
Closed Access | Times Cited: 53
Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models
Jiayu Chen, Lisang Liu, Kaiqi Guo, et al.
Applied Sciences (2024) Vol. 14, Iss. 14, pp. 5966-5966
Open Access | Times Cited: 4
A new hybrid prediction model of PM2.5 concentration based on secondary decomposition and optimized extreme learning machine
Hong Yang, Junlin Zhao, Guohui Li
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 44, pp. 67214-67241
Closed Access | Times Cited: 19
Short-Term Load Forecasting Based on VMD and Deep TCN-Based Hybrid Model with Self-Attention Mechanism
Qingliang Xiong, Mingping Liu, Yuqin Li, et al.
Applied Sciences (2023) Vol. 13, Iss. 22, pp. 12479-12479
Open Access | Times Cited: 9
Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization
Xue‐Bo Jin, Hongxing Wang, Xiaoyi Wang, et al.
Complexity (2020) Vol. 2020, pp. 1-14
Open Access | Times Cited: 24
Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm
Suqi Zhang, Ningjing Zhang, Ziqi Zhang, et al.
Energies (2022) Vol. 15, Iss. 23, pp. 9197-9197
Open Access | Times Cited: 12
Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan
Muhammad Sibtain, Xianshan Li, Muhammad Imran Azam, et al.
Polish Journal of Environmental Studies (2020) Vol. 30, Iss. 1, pp. 369-384
Open Access | Times Cited: 18
Short-term load forecasting based on IPSO-DBiLSTM network with variational mode decomposition and attention mechanism
Yuan Huang, Zheng Huang, Junhao Yu, et al.
Applied Intelligence (2022) Vol. 53, Iss. 10, pp. 12701-12718
Closed Access | Times Cited: 11
Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM
Gang Zhang, Hongchi Liu, Pingli Li, et al.
Complexity (2020) Vol. 2020, pp. 1-20
Open Access | Times Cited: 14
A Power Load Forecasting Model Based on FA-CSSA-ELM
Zuoxun Wang, Xinheng Wang, Chunrui Ma, et al.
Mathematical Problems in Engineering (2021) Vol. 2021, pp. 1-14
Open Access | Times Cited: 11
The Sunspot Number Forecasting Using a Hybridization Model of EMD, LSTM and Attention Mechanism
Jianzhong Yang, Nian Fu, Huirong Chen
IEEJ Transactions on Electrical and Electronic Engineering (2023) Vol. 18, Iss. 11, pp. 1791-1798
Closed Access | Times Cited: 4
Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management
Yu Fan, Lei Wang, Qiaoyong Jiang, et al.
Energies (2022) Vol. 15, Iss. 12, pp. 4198-4198
Open Access | Times Cited: 7
Application of Variational Mode Decomposition and Deep Learning in Short-Term Power Load Forecasting
Ping Yu, Jie Fang, Yubao Xu, et al.
Journal of Physics Conference Series (2021) Vol. 1883, Iss. 1, pp. 012128-012128
Open Access | Times Cited: 7
Probabilistic Forecasting of Residential Energy Consumption Based on SWT-QRTCN-ADSC-NLSTM Model
Ning Jin, Linlin Song, Gabriel Jing Huang, et al.
Information (2023) Vol. 14, Iss. 4, pp. 231-231
Open Access | Times Cited: 2
Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques
Fuqiang Li, Shiying Zhang, Wenxuan Li, et al.
Sustainability (2019) Vol. 11, Iss. 24, pp. 6954-6954
Open Access | Times Cited: 4
Multifactor load modeling for smart grids: An urban area case study
Ernad Jabandžić, Tatjana Konjić, Sabina Baraković
Electric Power Systems Research (2024) Vol. 233, pp. 110452-110452
Closed Access
Load Management Framework in Smart Grids: A Meta-Analysis and Review
Ernad Jabandžić, Tatjana Konjić, Dženana Tomašević
IETE Technical Review (2021) Vol. 39, Iss. 4, pp. 737-755
Closed Access | Times Cited: 3
Power abnormal data detection and short-term load prediction model construction based on genetic algorithm
Ning Zhang, Chenguang Yang
(2023), pp. 121-121
Closed Access
Short Term Power Load Forecasting Method Based on VMD-ResNet
Dahua Li, Wenkai Chen, Sun Wang, et al.
2022 4th International Conference on Power and Energy Technology (ICPET) (2023), pp. 976-981
Closed Access
Short-Term Load Forecasting Method based on Empirical Wavelet Decomposition and BLSTM Neural Networks
Xiaoyu Zhang, Stefanie Kuenzel, Nicolò Colombo, et al.
(2021)
Open Access
Short-Term Load Forecasting Method based on Empirical Wavelet Decomposition and BLSTM Neural Networks
Xiaoyu Zhang, Stefanie Kuenzel, Nicolò Colombo, et al.
(2021)
Open Access
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