# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
### Literature Survey  
Existing research on applying ML methods to solve UC problems can be broadly classified into two main categories: \"direct\" ML methods and \"indirect\" ML methods [15]. The \"direct\" ML methods refer to end-to-end learning approaches, where ML models are trained to generate solutions directly from input examples. Reference [16] proposes a multiagent fuzzy reinforcement learning model, where generator sets are treated as participants in a reinforcement learning scenario to solve UC problems. However, this model may face limitations when applied to large-scale systems. In [17], regression trees and random forests are used to predict the difference between the MILP solution and the linear programming (LP) relaxed solution for the UC problem. The LP relaxed solution is then adjusted based on this prediction. Nevertheless, this method may face challenges when the difference between the two solutions is large. Reference [18] clusters the historical data of the IEEE 118-bus system into multiple patterns and trains a gated recurrent unit (GRU) to predict the solutions for new instances. In [19], K-nearest neighbor (KNN), random forest, and support vector machines (SVM) were used to predict the on/off status of units in the 39-bus system. A difficulty is that the methods often struggle to maintain prediction accuracy when applied to scenarios outside their training data. In practical research, exploring the direct relationship between load and solutions to UC problems is often challenging. As a result, \"direct\" ML methods frequently struggle to deliver high-quality solutions. In this context, \"indirect\" ML methods have gained significant attention, as they can improve solution efficiency by providing supplementary decision-making information to the UC algorithm or integrating directly with it. Reference [20] proposes a joint branch selection policy that groups discrete variables to construct separate predictors for each group. For large-scale problems, where many possible branch choices exist, this method is ineffective. Reference [21] employs graph convolutional neural networks to learn the full strong branching strategy for B&B decisions, thereby reducing the solution time for the UC problem. However, the method exhibits insufficient prediction accuracy and achieves only a limited reduction in the number of branch nodes. In [22], the unconstrained problem is solved first, followed by the addition of violated constraints, which are then learned using KNN. This approach has limited applicability, and it is not easy to design representative scenarios. Reference [23] proposes an integrated framework that combines data-driven and variable aggregation approaches. ML techniques are used to predict the difficulty of solving UC instances, determining whether heuristics should be applied to accelerate the MILP solver. While this approach enhances problem-solving efficiency, it may compromise solution quality. Reference [24] learns the on/off status of units in the Polish system under different scenarios, reducing the number of binary variables by fixing the on/off status of some units. However, this approach cannot predict instances beyond the existing scenarios. Existing research has demonstrated the significant potential of ML technology in solving UC problems, but several intractable challenges remain. Beyond the fact that computational efficiency and solution quality are not ideal, two prominent shortcomings persist. On the one hand, a large number of training problem instances are required. For a specific system, thousands or even hundreds of thousands of training examples may be needed, resulting in prohibitive sample acquisition and training costs, which are impractical in real-world applications. On the other hand, existing methods frequently demonstrate limited generalization capability, as they are typically tailored to specific systems.