# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 3 Results
### Evaluation and Comparison
#### 3.2.3 Visual Methods for Evaluation of the Synthetic Data
This indicates that the WGAN-GP architecture effectively captures both global and local structure in the household dataset, making it the most robust among the four models for generating realistic synthetic data. Figure 15: t-SNE analysis of the Industrial dataset using Vanilla-GAN (**a**), WGAN-GP (**b**), Conv1D-GAN (**c**), and Conv1D-WGAN-GP (**d**). While Conv1D-WGAN-GP consistently demonstrates strong visual overlap between real and synthetic samples in t-SNE projections, it is important to note that WGAN-GP performs equally well or better on some datasets, such as Industrial and Household. In these cases, the synthetic data generated by WGAN-GP is thoroughly interspersed with real data, indicating high distributional fidelity, even without the convolutional structure of the proposed model. The Pig Farm dataset illustrates the varying capability of GAN models in capturing complex, overlapping data patterns, as evident in Figure 17.  
Figure 16: t-SNE analysis of the Household dataset using Vanilla-GAN (**a**), WGAN-GP (**b**), Conv1D-GAN (**c**), and Conv1D-WGAN-GP (**d**). Figure 17: t-SNE analysis of the Pig Farm dataset using Vanilla-GAN (**a**), WGAN-GP (**b**), Conv1D-GAN (**c**), and Conv1D-WGAN-GP (**d**). The Conv1D-GAN model shows partial mixing of synthetic and real points, but distinct clusters of real and synthetic data remain, especially on opposite ends of the space. This separation indicates that the model has learned some general structure but fails to cover the full range of real data diversity. In the Vanilla-GAN plot, the separation is more pronounced, with large synthetic clusters diverging from real ones. The lack of integration among distributions confirms that Vanilla-GAN underperforms in modeling this dataset. The Conv1D-WGAN-GP plot demonstrates much better overlap, with real and synthetic data points interspersed more uniformly across the space. Though minor shifts still appear in some regions, the general structure of the real distribution is reasonably well replicated. Finally, in the WGAN-GP model, the synthetic and real data distributions are thoroughly mixed across all visible clusters, suggesting the model captures both local and global properties of the original data. Analyzing histograms per hour is critically important because it allows for a visual and quantitative comparison of how well the synthetic data replicate the real data's distribution at each hour. Histogram plots make it easy to spot specific hours where the synthetic data fail to capture the correct distributional shape, such as missing peaks, incorrect skewness, or differences in spread and variability. Figure 18 shows a comparative visualization of the real and synthetic data from the ERGAN dataset for the 20 th hour. The hour for the visualization (20 th) is chosen based on its interesting distribution of the real data, as there is a noteworthy spike near the maximum value of 1.0. The Conv1D-GAN model captures the general shape of the real distribution reasonably well. It reflects the broad peak of real values around the mid-range (0.3 to 0.6) but fails to replicate the sharp spike in real data near the maximum value (1.0). This underrepresentation of high-load values suggests that while the model learns the central trend, it struggles to generate extreme load cases. Figure 18: Histograms of the 20 th hour for the ERGAN dataset using Vanilla-GAN (**a**), WGAN-GP (**b**), Conv1D-GAN (**c**), and Conv1D-WGAN-GP (**d**). In contrast, the Vanilla-GAN produces a smoother, bell-shaped synthetic distribution centered around 0.5. Although it aligns reasonably well with the real data in the central range, it noticeably fails to capture the asymmetry of the real distribution and completely misses the accumulation of values near 1.0. As a result, the synthetic data appear too regular and idealized compared to the more varied and skewed real distribution. This indicates that the Vanilla-GAN lacks the capacity to model the complex distribution tails effectively.