# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 6 Conclusions and Prospects  
This paper proposes a data-driven optimal scheduling framework for new integrated energy systems with limited historical data. Considering the spatial correlation and meteorological similarity, the maximum mean difference method is used to find power similar to power stations, and the capacity discounting algorithm generates new energy power historical data. Based on the existing electric load data for different day types, we generate historical data by randomly sampling the load at the exact moment and combining the generated load data with the PV output data to construct a comprehensive historical  
Figure 12: Time-of-day tariffs and SOC curves.  
Figure 13: Single-step operating cost and grid interaction power. (a) Comparison of operating costs. (b) Comparison of power interaction with the power grid.  
dataset. For the sample imbalance problem in the small-sample scenario, the oversampling method is employed to enhance the data for the scarce samples, and an XGBoost PV output prediction model is established to predict the PV output in real-time. Finally, the optimal scheduling model is transformed into a Markov decision process and solved using the Deep Deterministic Policy Gradient (DDPG) algorithm. The framework can effectively solve the problem of insufficient training samples in deep reinforcement learning by integrating spatial correlation analysis, meteorological feature transfer, and dynamic data enhancement. An example study shows that, through multidimensional data enhancement and improved prediction scheduling coordination, the proposed framework can improve the accuracy of PV output prediction and the training effect of deep reinforcement learning intelligence in data-scarce scenarios, and the method can effectively satisfy the power constraint regulation and reduce the operation cost compared to the no data expansion scenario.  
However, the current framework focuses on electricity-centered dispatch. Future extensions of the model to include heat-hydrogen gas multi-energy dynamics and demand response mechanisms will enhance its practical applicability.  
Conceptualization, Y.L. and J.C. ([PERSON]); Data curation, J.C. ([PERSON]); Formal analysis, K.Z.; Investigation, J.C. ([PERSON]); Methodology, Y.L.; Project administration, Y.S.; Resources, J.C. ([PERSON]); Software, Y.L. and J.C. ([PERSON]); Supervision, J.C. ([PERSON]); Validation, Y.L., K.Z., Y.S. and J.C. ([PERSON]); Writing--original draft, Y.L. and K.Z.; Writing--review and editing, Y.L. and K.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of State Grid Corporation of China, grant number 5400-202321572A-3-2-ZN.  
Data Availability StatementData are available on request from the authors.  
Conflicts of InterestAuthor [PERSON] was employed by the company State Grid Jbei Electric Power 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.