# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 2 Materials and Methods
### Benchmark Models
Its robustness across different architectures, including convolutional and recurrent networks, and lower sensitivity to hyperparameters further contribute to its appeal for practical applications, and therefore will be used in this study. As a benchmark, the WGAN-GP offers a robust and reliable alternative to vanilla GANs. It sets a high-performance baseline for fidelity and training stability, and is widely used in research as a go-to model when evaluating new GAN variations. The benchmark architecture is described in Table 3.  
While vanilla GANs and WGAN-GP offer strong baselines for generative modeling, they were originally designed for static data, such as images or tabular datasets. Electrical load profiles, however, are inherently sequential and time-dependent, meaning that each value in a 24 h profile is closely related to the hours before and after it. To better capture these local temporal dependencies, it is valuable to include convolutional GANs (Conv1D-GANs) as part of the benchmark models for synthetic time-series generation. Conv1D-GANs incorporate one-dimensional convolutional layers into the generator and discriminator. This architecture enables the model to learn local patterns across short time windows, such as hourly consumption spikes or dips. By applying temporal convolution, the generator can effectively model translation-invariant patterns, such as the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Characteristic** & **Value** \\ \hline \multirow{5}{*}{Generator} & 1 st layer: & Densely connected: 128 units; activation—LeakyReLU \\  & 2 nd layer: & batch normalization \\  & 3 rd layer: & Densely connected: 256 units; activation—LeakyReLU \\  & 4 th layer: & batch normalization \\  & 5 th layer: & Densely connected: 128 units; activation—LeakyReLU \\  & Output: & Densely connected: 24 units; activation—sigmoid \\  & Loss function: & Wasserstein loss \\  & [PERSON] & [PERSON] (adaptive moment estimation): learning rate = 0.0002; \\ \hline \multirow{5}{*}{Discriminator} & 1 st layer: & Densely connected: 128 units; activation—LeakyReLU \\  & 2 nd layer: & dropout; dropout rate = 0.3 \\ \cline{1-1}  & 3 rd layer: & Densely connected: 64 units; activation—LeakyReLU \\ \cline{1-1}  & 4 th layer: & dropout; dropout rate = 0.3 \\ \cline{1-1}  & Output: & Densely connected: 1 unit; activation—none \\ \cline{1-1}  & Loss function: & Wasserstein loss \\ \cline{1-1}  & [PERSON] & [PERSON] (adaptive moment estimation): learning rate = 0.0002; \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Architecture of the WGAN-GP model used for benchmarking. morning ramp-up or evening decline in energy usage. Unlike feed-forward GANs, which treat the 24-dimensional vector as an unordered feature set, Conv1D-GANs preserve the sequential order while capturing local correlations through sliding filters, making them well-suited for time-series data such as electrical load profiles. The benchmark used in this study follows the architectural principles described in recent convolutional GAN literature and is summarized in Table 4.