  Day-ahead operations 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 security-constrained unit commitment (SCUC). 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, 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. Simulation results demonstrate a high training accuracy to identify
commitment schedule while FL and post-process ensure ML predictions do not lead
to infeasible solutions with minimal loss in solution quality.
