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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:

A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting
Wenyu Zhang, Qian Chen, Jianyong Yan, et al.
Energy (2021) Vol. 236, pp. 121492-121492
Closed Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach
Yang Li, Ruinong Wang, Yuanzheng Li, et al.
Applied Energy (2022) Vol. 329, pp. 120291-120291
Open Access | Times Cited: 167
Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms
Sadegh Afzal, Behrooz M. Ziapour, Afshar Shokri, et al.
Energy (2023) Vol. 282, pp. 128446-128446
Closed Access | Times Cited: 117
A comprehensive review on deep learning approaches for short-term load forecasting
Yavuz Eren, İbrahim Beklan Küçükdemiral
Renewable and Sustainable Energy Reviews (2023) Vol. 189, pp. 114031-114031
Open Access | Times Cited: 70
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Faiaz Ahsan, Nazia Hasan Dana, Subrata K. Sarker, et al.
Protection and Control of Modern Power Systems (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 58
Short-term electrical load forecasting using hybrid model of manta ray foraging optimization and support vector regression
Siwei Li, Xiangyu Kong, Yue Liang, et al.
Journal of Cleaner Production (2023) Vol. 388, pp. 135856-135856
Closed Access | Times Cited: 43
AI‐organoid integrated systems for biomedical studies and applications
Sudhiksha Maramraju, Andrew Kowalczewski, Anirudh Kaza, et al.
Bioengineering & Translational Medicine (2024) Vol. 9, Iss. 2
Open Access | Times Cited: 18
A novel machine learning-based electricity price forecasting model based on optimal model selection strategy
Wendong Yang, Shaolong Sun, Hao Yan, et al.
Energy (2021) Vol. 238, pp. 121989-121989
Closed Access | Times Cited: 102
Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting
Yanmei Huang, Najmul Hasan, Changrui Deng, et al.
Energy (2021) Vol. 239, pp. 122245-122245
Open Access | Times Cited: 75
Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization
Jiaqi Shi, Chenxi Li, Xiaohe Yan
Energy (2022) Vol. 262, pp. 125295-125295
Closed Access | Times Cited: 62
A taxonomy of machine learning applications for virtual power plants and home/building energy management systems
Seppo Sierla, Mahdi Pourakbari‐Kasmaei, Valeriy Vyatkin
Automation in Construction (2022) Vol. 136, pp. 104174-104174
Open Access | Times Cited: 47
Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting
Xun Xiao, Huadong Mo, Yinan Zhang, et al.
Energy (2022) Vol. 246, pp. 123418-123418
Closed Access | Times Cited: 43
Current status, challenges, and prospects of data-driven urban energy modeling: A review of machine learning methods
Prajowal Manandhar, Hasan Rafiq, Edwin Rodríguez-Ubiñas
Energy Reports (2023) Vol. 9, pp. 2757-2776
Open Access | Times Cited: 32
Complex artificial intelligence models for energy sustainability in educational buildings
Rasikh Tariq, Awsan Mohammed, Adel Alshibani, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 10
A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer
Dongxue Zhang, Shuai Wang, Yuqiu Liang, et al.
Energy (2022) Vol. 264, pp. 126172-126172
Closed Access | Times Cited: 29
Integrated Approaches in Resilient Hierarchical Load Forecasting via TCN and Optimal Valley Filling Based Demand Response Application
A. Selim Türkoğlu, Burcu Erkmen, Yavuz Eren, et al.
Applied Energy (2024) Vol. 360, pp. 122722-122722
Closed Access | Times Cited: 8
Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review
Radhika Chandrasekaran, Senthil Kumar Paramasivan
Archives of Computational Methods in Engineering (2024)
Closed Access | Times Cited: 7
Graph convolutional network-based aggregated demand response baseline load estimation
Peng Tao, Fei Xu, Zengbo Dong, et al.
Energy (2022) Vol. 251, pp. 123847-123847
Closed Access | Times Cited: 27
Hyperparameter optimization of support vector machine using adaptive differential evolution for electricity load forecasting
M. Zulfiqar, Muhammad Kamran, Muhammad Babar Rasheed, et al.
Energy Reports (2022) Vol. 8, pp. 13333-13352
Open Access | Times Cited: 26
A novel building energy consumption prediction method using deep reinforcement learning with consideration of fluctuation points
Wei Jin, Qiming Fu, Jianping Chen, et al.
Journal of Building Engineering (2022) Vol. 63, pp. 105458-105458
Open Access | Times Cited: 25
Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting
M. Zulfiqar, Kelum A. A. Gamage, Muhammad Kamran, et al.
Sensors (2022) Vol. 22, Iss. 12, pp. 4446-4446
Open Access | Times Cited: 23
Predictive control of power demand peak regulation based on deep reinforcement learning
Qiming Fu, Lu Liu, Lifan Zhao, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 106992-106992
Closed Access | Times Cited: 15
A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid
Camille Franklin Mbey, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, et al.
Applied Computational Intelligence and Soft Computing (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 6
Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems
Camille Franklin Mbey, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum, et al.
Cogent Engineering (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 6
Deep reinforcement learning based interpretable photovoltaic power prediction framework
Rongquan Zhang, Siqi Bu, Min Zhou, et al.
Sustainable Energy Technologies and Assessments (2024) Vol. 67, pp. 103830-103830
Closed Access | Times Cited: 6
EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting
Faiza Mehmood, Muhammad Usman Ghani Khan, Hina Ghafoor, et al.
Applied Energy (2022) Vol. 324, pp. 119754-119754
Closed Access | Times Cited: 20
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