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:

Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning
Sichen Li, Weihao Hu, Di Cao, et al.
Applied Energy (2023) Vol. 346, pp. 121359-121359
Closed Access | Times Cited: 7

Showing 7 citing articles:

A Novel MADRL With Spatial-Temporal Pattern Capturing Ability for Robust Decentralized Control of Multiple Microgrids Under Anomalous Measurements
Sichen Li, Weihao Hu, Di Cao, et al.
IEEE Transactions on Sustainable Energy (2024) Vol. 15, Iss. 3, pp. 1872-1884
Closed Access | Times Cited: 4

An innovative end-to-end PINN-based solution for rapidly simulating homogeneous heat flow problems: An adaptive universal physics-guided auto-solver
Yijie Zhao, Donghe Li, Chunhui Wang, et al.
Case Studies in Thermal Engineering (2024) Vol. 56, pp. 104277-104277
Open Access | Times Cited: 3

Two-timescale autonomous energy management strategy based on multi-agent deep reinforcement learning approach for residential multicarrier energy system
Bin Zhang, Weihao Hu, Amer M. Y. M. Ghias, et al.
Applied Energy (2023) Vol. 351, pp. 121777-121777
Closed Access | Times Cited: 9

Cooperative optimal dispatch of multi-microgrids for low carbon economy based on personalized federated reinforcement learning
Ting Yang, Zheming Xu, Shijie Ji, et al.
Applied Energy (2024) Vol. 378, pp. 124641-124641
Closed Access

Energy efficiency and conservation using machine learning
Akshay Bhuvaneswari Ramakrishnan, T. S. Murugesh, Sini Raj Pulari, et al.
Elsevier eBooks (2024), pp. 69-78
Closed Access

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