# 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
Additionally, the synthetic values are more evenly spread across the 0.1-0.5 range, resulting in a smoother curve that does not fully reflect the abrupt drop-off seen in the real distribution. This indicates moderate success in capturing the general structure but a lack of precision in replicating the intensity of the low-load spike. The Vanilla-GAN performs similarly, with slightly better alignment near the zero-load region. It reproduces the initial peak more closely but still falls short of matching its magnitude. The synthetic data again displays a smoother and more gradual decline, missing the sharp dips and sparse outliers in the real data. While it succeeds in following the overall skewed pattern, the generated distribution appears overly regular and lacks the noise and asymmetry observed in the real data. The Conv1D-WGAN-GP model performs better than the previous two in capturing the core structure of the distribution. It matches the sharp rise at the beginning and more accurately represents the frequencies of mid-range values. However, some fluctuations remain mismatched, and the model generates slightly more synthetic values in the range above 0.5 than the real distribution suggests. Still, among the first three, this model achieves the most balanced synthesis, showing a credible representation of the sharp left-skew and long tail. The WGAN-GP model delivers the closest overall approximation. It replicates the sharp spike at near-zero load with the highest fidelity among the models and produces a realistic decline into the mid-range values. Its synthetic distribution follows the real distribution's fluctuations more faithfully, without over-smoothing or underestimating low-load dominance. The tail also appears better matched, with a proper drop in counts beyond 0.6. The Pig Farm dataset is of high interest, as this is a typical dataset that is considered in this methodology--a small dataset with a load profile from one complex for one year. This is also an industrial dataset, but as evident from Figure 21, there are no clear patterns in the load profiles. Therefore, the chosen hour (9 th hour) for the histograms of the models is based on asymmetric distributions of the real data. The Conv1D-GAN captures the central part of the real distribution fairly well, particularly in the range between 0.2 and 0.5. The synthetic values align moderately with the real ones, but the model underrepresents higher load values above 0.6 and also overrepresents some lower values between 0.1 and 0.2. While the model reproduces the general shape of the distribution, it misses the variability and irregularity seen in the real data, especially toward the tail ends. The Vanilla-GAN performs less convincingly. It generates a concentration of synthetic values around 0.2 to 0.3 and again near 1.0, overestimating low-to-mid values and producing an unrealistic tail spike. The real data show a more gradual drop-off after the peak, but the synthetic distribution here appears biased and noisy, introducing artificial structure that is not present in the true distribution. The mismatch is especially visible at both extremes, suggesting that the Vanilla-GAN overfits local patterns without generalizing well. The Conv1D-WGAN-GP model offers a significantly better match to the real distribution. The synthetic values track both the main peak near 0.3 and the longer tail toward 0.6 quite well. The histogram shapes are aligned, with some divergence in the finer local fluctuations. This model can replicate the skew, the spread, and even parts of the multi-modal structure with better fidelity than the previous two. However, minor mismatches remain, particularly in the underrepresented regions between 0.6 and 0.8. The WGAN-GP model also performs quite well with a realistic synthetic distribution that closely mirrors the shape and spread of the real data. It captures the dominant modes, spreads values appropriately across the 0.2 to 0.6 range, and avoids producing outliers at the extremes.