首页|Reports from Georgia Institute of Technology Highlight Recent Findings in Machine Learning (Learning Regionally Decentralized Ac Optimal Power Flows With Admm)
Reports from Georgia Institute of Technology Highlight Recent Findings in Machine Learning (Learning Regionally Decentralized Ac Optimal Power Flows With Admm)
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Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Atlanta, Georgia, by NewsRx journalists, research stated, "One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e.g., wind/solar), dispatchable devices (e.g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization. The Alternating Direction Method of Multipliers (ADMM) has been widely used in the community to address such decentralized optimization problems and, in particular, the AC Optimal Power Flow (AC-OPF)." Financial support for this research came from National Science Foundation (NSF).
AtlantaGeorgiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningGeorgia Institute of Technology
NETL
NSTL
IEEE
