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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:

Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm
Tian Gao, Dongxiao Niu, Zhengsen Ji, et al.
Energy (2022) Vol. 261, pp. 125328-125328
Closed Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition
Wenchuan Wang, Qi Cheng, Kwok‐wing Chau, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129460-129460
Closed Access | Times Cited: 43
A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Sujan Ghimire, Thong Nguyen‐Huy, Mohanad S. AL‐Musaylh, et al.
Energy (2023) Vol. 275, pp. 127430-127430
Open Access | Times Cited: 43
Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation
Robertas Damaševičius, Luka Jovanovic, Aleksandar Petrović, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1795-e1795
Open Access | Times Cited: 27
Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics
Junjie Wang, Wenyu Huang, Yuanping Ding, et al.
Energy (2025), pp. 134518-134518
Closed Access | Times Cited: 2
Economic-environmental dispatch of isolated microgrids based on dynamic classification sparrow search algorithm
Guodong Xie, Mengjian Zhang, Deguang Wang, et al.
Expert Systems with Applications (2025) Vol. 271, pp. 126677-126677
Closed Access | Times Cited: 2
Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks
Niaz Bashiri Behmiri, Carlo Fezzi, Francesco Ravazzolo
Energy (2023) Vol. 278, pp. 127831-127831
Open Access | Times Cited: 37
A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network
Tianhong Liu, Shengli Qi, Xianzhu Qiao, et al.
Energy (2023) Vol. 288, pp. 129904-129904
Closed Access | Times Cited: 27
Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting
Han Wu, Yan Liang, Jiani Heng
Applied Energy (2023) Vol. 339, pp. 120995-120995
Closed Access | Times Cited: 23
Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts
Mie Wang, Feixiang Ying, Qianru Nan
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108435-108435
Closed Access | Times Cited: 12
A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports
Di Zhang, Xinyuan Li, Chengpeng Wan, et al.
Maritime Economics & Logistics (2024) Vol. 26, Iss. 1, pp. 44-73
Closed Access | Times Cited: 9
Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm
Huanhuan Hu, Shufen Gong, Bahman Taheri
Heliyon (2024) Vol. 10, Iss. 6, pp. e27353-e27353
Open Access | Times Cited: 9
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: 9
Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a university community
Chengyu Zhang, Zhiwen Luo, Yacine Rezgui, et al.
Energy (2024) Vol. 294, pp. 130896-130896
Closed Access | Times Cited: 8
Advancing energy efficiency: Machine learning based forecasting models for integrated power systems in food processing company
Seray MİRASÇI, Sara Uygur, Aslı Aksoy
International Journal of Electrical Power & Energy Systems (2025) Vol. 165, pp. 110445-110445
Open Access | Times Cited: 1
Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods
Paweł Pełka
Energies (2023) Vol. 16, Iss. 2, pp. 827-827
Open Access | Times Cited: 21
Point and interval prediction for significant wave height based on LSTM-GRU and KDE
Mie Wang, Feixiang Ying
Ocean Engineering (2023) Vol. 289, pp. 116247-116247
Closed Access | Times Cited: 20
The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm and attention mechanism
Xiwen Cui, Xiaoyu Yu, Dongxiao Niu
Energy (2023) Vol. 288, pp. 129714-129714
Closed Access | Times Cited: 17
Enhancing multi-scenario data-driven energy consumption prediction in campus buildings by selecting appropriate inputs and improving algorithms with attention mechanisms
Chengyu Zhang, Zhiwen Luo, Yacine Rezgui, et al.
Energy and Buildings (2024) Vol. 311, pp. 114133-114133
Closed Access | Times Cited: 7
Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks
Miaosen Hu, Guoqiang Zheng, Zhonge Su, et al.
Energy (2024) Vol. 303, pp. 131951-131951
Closed Access | Times Cited: 6
Improving building energy consumption prediction using occupant-building interaction inputs and improved swarm intelligent algorithms
Chengyu Zhang, Liangdong Ma, Han Xing, et al.
Journal of Building Engineering (2023) Vol. 73, pp. 106671-106671
Open Access | Times Cited: 14
Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review
Julius Adinkrah, Francis Kemausuor, Eric Tutu Tchao, et al.
Renewable and Sustainable Energy Reviews (2024) Vol. 210, pp. 115161-115161
Closed Access | Times Cited: 5
Predicting Electricity Consumption in the Kingdom of Saudi Arabia
Marwa Salah EIDin Fahmy, Farhan Ahmed, Farah Durani, et al.
Energies (2023) Vol. 16, Iss. 1, pp. 506-506
Open Access | Times Cited: 12
Workshop layout optimization method based on sparrow search algorithm: a new approach
Shenghao Bi, Liangshan Shao, Jiatian Zheng, et al.
Journal of Industrial and Production Engineering (2024) Vol. 41, Iss. 4, pp. 324-343
Closed Access | Times Cited: 4
Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm
Yun Zhu, Ya-Hui Guo, Tianyu Hu, et al.
International Journal of Energy Research (2024) Vol. 2024, pp. 1-25
Open Access | Times Cited: 4
Electricity demand forecasting in Zhangjiajie under the background of “dual carbon”
Lujie Ren, Jiaxi Li, Ting Zeng, et al.
Journal of Physics Conference Series (2025) Vol. 2960, Iss. 1, pp. 012009-012009
Open Access
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