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Security-constrained unit commitment (SCUC) is a computationally complexprocess utilized in power system day-ahead scheduling and market clearing. SCUCis run daily and requires state-of-the-art algorithms to speed up the process.The constraints and data associated with SCUC are both geographically andtemporally correlated to ensure the reliability of the solution, which furtherincreases the complexity. In this paper, an advanced machine learning (ML)model is used to study the patterns in power system historical data, whichinherently considers both spatial and temporal (ST) correlations inconstraints. The ST-correlated ML model is trained to understand spatialcorrelation by considering graph neural networks (GNN) whereas temporalsequences are studied using long short-term memory (LSTM) networks. Theproposed approach is validated on several test systems namely, IEEE 24-Bussystem, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina(SC) 500-Bus system. Moreover, B-{\theta} and power transfer distributionfactor (PTDF) based SCUC formulations were considered in this research.Simulation results demonstrate that the ST approach can effectively predictgenerator commitment schedule and classify critical and non-critical lines inthe system which are utilized for model reduction of SCUC to obtaincomputational enhancement without loss in solution quality

Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment