Journal article
Sustainable Energy, Grids and Networks
PhD - Research Fellow
National University of Singapore
APA
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Dindar, B., Saner, C. B., Cakmak, H. K., & Hagenmayer, V. Machine Learning-Driven Multi-Agent-Based AC Optimal Power Flow with Effective Dataset Creation for Data Privacy and Interoperability. Sustainable Energy, Grids and Networks.
Chicago/Turabian
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Dindar, Burak, Can Berk Saner, Huseyin Kemal Cakmak, and Veit Hagenmayer. “Machine Learning-Driven Multi-Agent-Based AC Optimal Power Flow with Effective Dataset Creation for Data Privacy and Interoperability.” Sustainable Energy, Grids and Networks (n.d.).
MLA
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Dindar, Burak, et al. “Machine Learning-Driven Multi-Agent-Based AC Optimal Power Flow with Effective Dataset Creation for Data Privacy and Interoperability.” Sustainable Energy, Grids and Networks, Accepted.
BibTeX Click to copy
@article{burak-a,
title = {Machine Learning-Driven Multi-Agent-Based AC Optimal Power Flow with Effective Dataset Creation for Data Privacy and Interoperability},
journal = {Sustainable Energy, Grids and Networks},
author = {Dindar, Burak and Saner, Can Berk and Cakmak, Huseyin Kemal and Hagenmayer, Veit},
howpublished = {Accepted}
}