# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 5 Case Studies
### Data Sample Expansion  
To select the features with the strongest correlation with the renewable energy output and increase the calculation speed, Pearson correlation coefficients were used to calculate the correlation between each meteorological feature and the renewable energy output. The results of the correlation calculation are shown in Table 1. As can be seen from the table, in terms of PV output, the correlation between radiation intensity and PV output is strong and is much higher than the other climatic factors. This is followed by humidity, and the remaining influences have low correlation with the PV output.  
To verify the effectiveness of the method proposed in this paper, historical PV data and electric load data are extracted for three weeks each in the summer, transition season, and winter to simulate the small-sample scenarios. Firstly, the three nearest field stations are selected as candidate source domains based on geographic locations, and the geographic locations of the target PV plants and selected PV plants are shown in Table 2. The PV plants are chosen based on their geographic locations. Multicore maximum mean difference (MK-MMD) is used for meteorological similarity to calculate the similarity between the source domain data and the target domain data, which is calculated separately for each day of missing data, and the similar power stations of the day are calculated and then discounted to the target PV power stations according to the capacity ratio. Through the correlation analysis between meteorological characteristics and renewable energy output, it can be seen that the main meteorological influencing factor is radiation intensity. To avoid a significant difference in the weights of multiple influencing factors, only radiation intensity can be selected as the basis for judging in the meteorological similarity calculation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristics** & **Temperature** & **Air Density** & **Radiation Intensity** & **Wind Speed** & **Precipitation** \\ \hline Correlation coefficient & 0.52 & 0.19 & 0.98 & \(-\)0.22 & \(-\)0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the correlation analysis between PV output and meteorological characteristics.  
The screened daily PV output data from the source domain are converted according to their respective capacities. The PV output data of the source and target domains, along with their corresponding meteorological features, are merged based on time alignment to construct a fusion dataset, which is used for model training. The electricity load data employs a dynamic enhancement strategy based on day type division, which fully considers the differences in load patterns between weekdays and weekends. It utilizes the uniform random sampling method to generate synthetic data that conform to the real distribution characteristics, thereby constructing an enhanced dataset applicable to the target region. The specific process is described in Section 2.  
Taking the summer scenario as an example, the newly built integrated energy system contains only 3 weeks (21 days) of historical PV data and electricity load data. After adopting the small-sample data expansion method based on Spatiotemporal Meteorological Augmentation (STMA), the data volume is expanded from 3 weeks to 1 year (365 days), of which the PV output is the same as that of the weekend (365 days), in which the PV output data are converted from the three surrounding PV power stations based on the daily meteorological similarity, and the electricity load data are synthesized using the dynamic enhancement strategy of day type division with uniform random sampling.