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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:

The Short‐Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China
Huanhe Dong, Ya Gao, Yong Fang, et al.
Computational Intelligence and Neuroscience (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 21

Showing 21 citing articles:

Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist
Jihoon Moon, Sungwoo Park, Seungmin Rho, et al.
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-20
Open Access | Times Cited: 18
A multi-information fusion model for short term load forecasting of an architectural complex considering spatio-temporal characteristics
Jiangjian Xie, Yujie Zhong, Tong Xiao, et al.
Energy and Buildings (2022) Vol. 277, pp. 112566-112566
Closed Access | Times Cited: 16
Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator
Joan Sebastian Caicedo-Vivas, Wilfredo Alfonso-Morales
Energies (2023) Vol. 16, Iss. 23, pp. 7878-7878
Open Access | Times Cited: 9
TimeGPT in load forecasting: A large time series model perspective
Wenlong Liao, Shouxiang Wang, Dechang Yang, et al.
Applied Energy (2024) Vol. 379, pp. 124973-124973
Open Access | Times Cited: 3
Comparative Analysis of different Machine learning Models for Load Forecasting
Rashmi Bareth, Matushree Kochar, Anamika Yadav
(2023)
Closed Access | Times Cited: 8
The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China
Jiuyun Sun, Huanhe Dong, Ya Gao, et al.
Computational Intelligence and Neuroscience (2021) Vol. 2021, pp. 1-8
Open Access | Times Cited: 14
Short-Term Demand Forecasting Method in Power Markets Based on the KSVM–TCN–GBRT
Guang Yang, Songhuai Du, Qingling Duan, et al.
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-13
Open Access | Times Cited: 9
A novel ultra-short-term wind power prediction method based on XA mechanism
Cheng Peng, Yiqin Zhang, Bowen Zhang, et al.
Applied Energy (2023) Vol. 351, pp. 121905-121905
Closed Access | Times Cited: 5
Double-machine-learning-based data-driven stochastic flow stress model for aluminium alloys at elevated temperatures
Baixi Chen, Yibo Yang, Lei Yang, et al.
Materials Today Communications (2022) Vol. 33, pp. 104506-104506
Closed Access | Times Cited: 7
An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods
Fatma Yaprakdal
Energies (2022) Vol. 16, Iss. 1, pp. 57-57
Open Access | Times Cited: 7
Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction
Akash Kumar, Bing Yan, Ace Bilton
Energies (2022) Vol. 15, Iss. 18, pp. 6721-6721
Open Access | Times Cited: 6
Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning
Hui Xu, Wei Pan, Xin Meng, et al.
Frontiers in Public Health (2022) Vol. 10
Open Access | Times Cited: 5
Short-term load forecasting for holidays based on similar days selecting and XGBoost model
Anping Huang, Juan Zhou, Cheng Tao, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 2
A Demand and Response Management Model Using Load Forecasting Technique for Smart Grid
Jatinder Kumar, Ashutosh Kumar Singh
(2023)
Closed Access | Times Cited: 2
Predictive models for short-term load forecasting in the UK’s electrical grid
Yusuf A. Sha’aban
PLoS ONE (2024) Vol. 19, Iss. 4, pp. e0297267-e0297267
Open Access
Quality control and improvement of GNSS-IR soil moisture robust inversion model
Yijie Li, Linyu Luo, Fei Guo, et al.
Advances in Space Research (2024)
Closed Access
Investigation of Electric Load Forecasting Methods: A Weka Application (Regression and Optimization)
Düzgün Akmaz
(2024), pp. 69-84
Closed Access
Short-Term Load Forecasting using Artificial Neural Network in Indonesia
Sylvia Jane Annatje Sumarauw
ILKOM Jurnal Ilmiah (2023) Vol. 15, Iss. 1, pp. 72-81
Open Access | Times Cited: 1
A Novel Short-Term Power-Load Forecasting Method Based on High-Dimensional Meteorological Data Dimensionality Reduction and Hybrid Deep Neural Network
Yan Shi, Siteng Wang, Luxi Zhang, et al.
Journal of Energy Engineering (2023) Vol. 149, Iss. 6
Closed Access | Times Cited: 1
An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition
Zhao Luo, Xinan Zhang, Yifu Chen, et al.
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-15
Open Access | Times Cited: 2
Methodology for Solving Forecasting Problems Based on Machine Learning Methods
Irina Kalinina, Aleksandr Gozhyj
Lecture notes on data engineering and communications technologies (2022), pp. 105-125
Closed Access
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