# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 5 Numerical Studies
### Dataset Setup and Performance Evaluation Metrics  
In this subsection, we outline the dataset setup and the method's performance evaluation metrics. Additionally, we present the size of the UC problem across various systems and the computational metrics of the B&B algorithm. The unit parameters for the standard testing systems are based on [34] and real-world provincial UC data in China. For UC problems involving a larger number of units, the 12-unit configuration is duplicated. The net load data for standard testing systems are sourced from annual load data in Ireland, available publicly in [35]. Although the unit parameters derived from [34] have been adaptively adjusted to better reflect real-world conditions, they remain overly idealized. In particular, there are significant disparities among different unit types, whereas units within the same type display highly similar parameters. To better validate the superiority and universality of the proposed method, numerical studies were also performed on practical test systems. For these systems, both unit parameters and load data are derived from real-world data in a provincial region of China. A specific number of thermal power plants (15 in total) are randomly selected, with the units within the same plant being identical. Any one of these units may serve as a representative for its corresponding plant. Additionally, all hydropower units (141 in total) are included to form various practical testing systems. It is important to note that the unit count refers exclusively to thermal power units. This study does not consider the on/off status of hydropower units, and standard testing systems do not include such units. During the training and testing phases, the net loads of the various systems are scaled appropriately by total unit generation capacities. The load sampling interval is set to 15 min, dividing a single day into 96 periods. Table 1 presents the sizes of the UC problem, measured by the number of binary and continuous variables, for different systems. In the training phase, for the enhanced pruning policy model, the hidden units of the neural network are set to \(\{32,\ 64,\ 16\}\). The model is trained for ten iterations, with input features as described in Section 4.2. The output corresponds to the probabilities of preserving and pruning nodes. In the testing phase, the performance of the method is evaluated using four metrics: the average node count, average calculation time, speedup, and optimality gap. The average node count refers to the total number of nodes explored to solve all tested UC problems, divided by the number of problems. The average calculation time indicates the total computation time for solving all tested UC problems, divided by the number of problems. The speedup metric quantifies the relative improvement in the computation speed of the particular algorithm compared to the B&B algorithm (e.g., 2\(\times\) means the particular algorithm solves the UC problem twice as fast as the B&B algorithm). This metric is determined by the total number of nodes explored by both algorithms during the UC problem resolution, covering all nodes explored during the iterative testing process. The optimality gap indicates the difference between the objective function value computed by the particular algorithm and the optimal solution provided by the B&B algorithm, representing the accuracy loss. In this study, the method's performance is evaluated during the testing phase using 50 daily load profiles (testing samples), each representing a UC problem. All metrics are averaged across the 50 testing daily load profiles. While searching the B&B tree for each testing daily load profile, the learned enhanced pruning policies are applied to each generated node. The average node count in the B&B tree typically increases exponentially with the number of units. As a result, 50 testing daily load profiles may contain thousands, or even tens of thousands, of testing nodes, providing a sufficient basis for performance evaluation.