Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed by the grid physical constraints. As grid integrate variable renewable sources, and new technologies such as long duration storage in the grid, UC must be optimally solved for multi-day horizons and potentially with greater frequency. Therefore, traditional MILP solvers increasingly struggle to compute solutions within these tightening operational time limits. To bypass these computational bottlenecks, this paper proposes a novel framework utilizing a transformer-based architecture to predict generator commitment schedules over a 72-hour horizon. Also, because raw predictions in highly dimensional spaces often yield physically infeasible results, the pipeline integrates the self-attention network with deterministic post-processing heuristics that systematically enforce minimum up/down times and minimize excess capacity. Finally, these refined predictions are utilized as a warm start for a downstream MILP solver, while employing a confidence-based variable fixation strategy to drastically reduce the combinatorial search space. Validated on a single-bus test system, the complete multi-stage pipeline achieves 100\% feasibility and significantly accelerates computation times. Notably, in approximately 20\% of test instances, the proposed model reached a feasible operational schedule with a lower overall system cost than relying solely on the solver.

翻译：维持电力供需的瞬时平衡对于系统可靠性和电网稳定性至关重要。系统运营商通过求解机组组合（UC）这一高维大规模混合整数线性规划（MILP）问题来实现这一目标，该问题受到电网物理约束的严格制约。随着电网整合可变可再生能源以及长期储能等新型技术，机组组合必须在多日时间尺度上以更高频率进行优化求解。因此，传统的MILP求解器在日益紧张的运行时限内愈发难以找到可行解。为突破这一计算瓶颈，本文提出了一种新颖框架，利用基于Transformer的架构来预测72小时内的发电机启停计划。此外，由于在高维空间中的原始预测结果往往会产生物理上不可行的解，该管线将自注意力网络与确定性后处理启发式算法相结合，系统性地强制满足最小启停时间约束并最小化冗余容量。最后，这些经过精炼的预测结果作为下游MILP求解器的热启动输入，并采用基于置信度的变量固定策略，大幅缩减组合搜索空间。经过单母线测试系统的验证，完整的多阶段管线实现了100%的可行性，并显著加快了计算速度。值得注意的是，在约20%的测试算例中，所提出的模型获得的可行运行方案具有比单纯依赖求解器更低的系统总成本。