首页|Findings on Machine Learning Detailed by Investigators at University of Houston (Feasibility Layer Aided Machine Learning Approach for Day-ahead Operations)
Findings on Machine Learning Detailed by Investigators at University of Houston (Feasibility Layer Aided Machine Learning Approach for Day-ahead Operations)
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Investigators discuss new findings in Machine Learning. According to news reporting from Houston, Texas, by NewsRx journalists, research stated, "Day-ahead operation involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as securityconstrained unit commitment (SCUC)." The news correspondents obtained a quote from the research from the University of Houston, "Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, South-Carolina (SC) synthetic grid 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation."
HoustonTexasUnited StatesNorth and Central AmericaCybersecurityCyborgsEmerging TechnologiesMachine LearningUniversity of Houston
NETL
NSTL
IEEE
