# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 6 Conclusions  
In this paper, we proposed branching acceleration for UC based on ML techniques to enhance the B&B algorithm through imitation learning and neural networks. This method addresses the UC cost minimization problem, which is typically formulated as a high-dimensional MILP problem in power systems. The effectiveness of the method is validated on both standard testing systems and practical testing systems from a provincial region in China. We have the following significant findings from numerical studies:  
* Branching acceleration for UC requires only a few dozen training instances to significantly reduce the computational complexity of the B&B algorithm with negligible accuracy loss, dramatically lowering data acquisition and training costs compared to existing methods that typically demand thousands or even hundreds of thousands of training samples.
* The enhanced pruning policy model exhibits outstanding generalization capability, effectively addressing the task mismatch challenge caused by dynamic changes in unit count--an issue that most existing methods struggle to overcome.
* The iterative algorithm ensures feasibility while mitigating the excessive search space issues common in other B&B improvements.
* Adjusting the iteration threshold further optimizes the method's performance and allows it to meet the specific requirements of different UC problems in terms of computational efficiency and accuracy loss.
* Branching acceleration for UC is relatively independent of the B&B algorithm's stages, enabling seamless integration with other B&B improvement methods to further enhance overall performance.  
These findings significantly enhance the practical value and applicability of branching acceleration for UC in real-world scenarios. This research is expected to make a meaningful contribution to advancing ML technology in the power systems domain. Future work may explore its application to various testing systems with a larger number of units, its combination with more advanced methods, or the development of specialized neural networks to enhance method performance--promising areas for further investigation.  
Conceptualization, C.Z. and Z.Q.; methodology, C.Z. and Z.Q.; software, C.Z.; validation, C.Z., Z.Q. and Y.S.; formal analysis, C.Z. and Z.Q.; investigation, C.Z.; resources, C.Z. and Z.Q.; data curation, C.Z., Z.Q. and Y.S.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z. and Z.Q.; visualization, C.Z.; supervision, Z.Q.; project administration, Z.Q.; funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China, grant number 52367003.  
Not applicable.  
Partial data analyzed in this study are included in the article. Other data analyzed during the current study are not publicly available due to privacy issues but are available from the corresponding author upon reasonable request.  
Author [PERSON] was employed by the company Guangxi Power Grid Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.