# 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  
From the t-SNE plots of the ERGAN dataset (Figure 14), which visualize the distribution of real and synthetic data in a 2D space, the Conv1D-GAN shows a noticeable gap between real and synthetic clusters, as follows: the real data spread broadly and form a complex, scattered cloud, while the synthetic data remain concentrated in a dense central blob. This suggests that the Conv1D-GAN struggles to reproduce the diversity of the original data distribution. Similarly, the Vanilla-GAN exhibits a distinct clustering of synthetic data in the center of the plot, with real data again forming a more expansive cloud around it. This indicates mode collapse or a lack of coverage of the full real data distribution, despite partial overlap. In contrast, both the Conv1D-WGAN-GP and WGAN-GP models achieve a better overlap between synthetic and real distributions. The Conv1D-WGAN-GP t-SNE plot reveals tighter integration of orange points within the blue cloud, implying improved fidelity and better coverage of the real data space. The WGAN-GP model demonstrates the most even spread, with the synthetic points largely embedded within the real distribution, suggesting that it has learned not only the central structure but also the outer variations in the real dataset. The t-SNE plots for the Industrial dataset are shown in Figure 15. The Conv1D-GAN model displays partial overlap between real and synthetic clusters, but several synthetic regions remain separated, suggesting the model fails to fully capture the distribution's complexity. Similarly, the Vanilla-GAN shows even more distinct segregation, with synthetic points clustering apart from real ones in multiple regions. This reinforces that the Vanilla-GAN struggles to generalize and exhibits mode collapse, generating synthetic samples from only a subset of the real data space. Figure 14: t-SNE analysis of the ERGAN dataset using Vanilla-GAN (a), WGAN-GP (b), Conv1D-GAN (c), and Conv1D-WGAN-GP (d). The Conv1D-WGAN-GP and WGAN-GP models again show significantly improved results. In the Conv1D-WGAN-GP plot, the synthetic points are more uniformly scattered among the real clusters, with less evidence of isolated synthetic groupings. This indicates the model has better learned the local structure of the data. The WGAN-GP model performs the best: synthetic data points are well integrated with real ones across all clusters, and there is no major separation among domains. This level of overlap points to a high-fidelity generative process that successfully mimics both the global and local distributional features of the original data. The t-SNE plots for the Household dataset (Figure 16) demonstrate clear structural patterns and allow a visually informative comparison of model performance. The Conv1D-GAN plot shows moderate mixing between real and synthetic data points, although some regions still display separation, particularly with clusters where synthetic points form localized groups that do not fully overlap with the real distribution. This suggests that the model learns the general structure but misses fine-grained variability. The Vanilla-GAN, by contrast, exhibits noticeable misalignment: clusters of synthetic data tend to shift away from real clusters, indicating limited generalization and possible mode collapse, especially where synthetic points dominate certain regions without a corresponding real data presence. The Conv1D-WGAN-GP shows a strong improvement, with synthetic and real samples overlapping extensively across the t-SNE space. While some minor cluster shifts still exist, the visual density and pattern alignment are well-preserved, signaling the model has learned the key distributional characteristics more faithfully. In the WGAN-GP model, the real and synthetic points are thoroughly intermingled throughout the space. There is no evident structural separation, and the clusters contain a balanced mix of both domains.