A transformer predicts unit commitment schedules over 72 hours, heuristics fix infeasibilities, and the output warm-starts a MILP solver to achieve 100% feasibility, faster runtimes, and lower costs in 20% of single-bus test cases.
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A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment
A transformer predicts unit commitment schedules over 72 hours, heuristics fix infeasibilities, and the output warm-starts a MILP solver to achieve 100% feasibility, faster runtimes, and lower costs in 20% of single-bus test cases.