# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 1 Introduction
At the same time, the volatility and uncertainty of renewable energy output in the integrated energy system seriously affect the energy supply and demand balance [25], energy storage system energy management, and demand response management, and accurate prediction of its output can effectively improve the scientificity of real-time intraday scheduling in IESs [26]. Many newly built IESs have been put into operation for a short period, and the lack of historical data for renewable energy sources makes it difficult to establish a high-precision output prediction model. Reference [27] proposed a hybrid prediction model based on gray correlation analysis (GRA) combined with a sparrow search algorithm (SSA) and a gray neural network model (GNNM), which is used to address the issue of low prediction accuracy in short-term PV power generation predictions with small sample sizes. Reference [28] proposed a PV power prediction framework based on data enhancement, which recovers PV power data by filtering erroneous data and utilizing a self-encoder network. The experiments show that this method can effectively discover relevant featuresand repair erroneous data. However, these methods are still insufficient for comprehensive small-sample PV prediction. Therefore, to solve the problem of missing data in small-sample scenarios of the deep reinforcement learning-based integrated energy system optimization scheduling problem, relying on the data-driven methodology, an XGBoost-DDPG optimization scheduling framework based on multidimensional data expansion for integrated energy systems in small-sample scenarios is proposed, which contains data expansion, photovoltaic prediction, and real-time optimization scheduling in small-sample scenarios. The experimental results demonstrate the effectiveness of our approach in addressing data scarcity challenges through multidimensional data augmentation and improved prediction-scheduling coordination. The main contributions of this paper are:  
1. A small-sample data expansion method based on spatiotemporal meteorological synergistic enhancement is proposed to address the problem of scarcity of historical data for newly built integrated energy systems, which integrates the dual screening mechanisms of geospatial correlation. The method integrates the dual screening mechanism of geospatial correlation (Haversine formula) and meteorological feature matching (MK-MMD algorithm). It migrates cross-station PV data through the capacity conversion formula. Dynamic load enhancement based on day-type segmentation generates synthetic load data that accurately reflects the distribution characteristics through uniform random sampling, effectively addressing data scarcity in small-sample scenarios. 2. For the optimization and scheduling problem of a newly built integrated energy system, an integrated energy system optimization and scheduling framework based on multidimensional data expansion in a small-sample scenario is proposed, which includes the expansion of PV data and load data in small-sample scenario, PV output prediction considering a sample imbalance, and real-time optimization and scheduling based on the DRL method. The framework implements a closed-loop solution, spanning data expansion, prediction modeling, and optimal scheduling. It provides a new technical path for integrating energy system optimal scheduling in small-sample scenarios. The rest of the paper is organized as follows. Section 2 presents a method for expanding small-sample data based on spatiotemporal meteorological synergistic enhancement. Section 3 describes the XGBoost PV power prediction method considering data imbalance in small-sample scenarios. Section 4 develops the optimal scheduling model of an integrated energy system based on deep reinforcement learning in small-sample scenarios. Case studies are presented in Section 5. Section 6 concludes.