# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 1 Introduction
Convolutional layers are used on those pixel-like matrices in the GAN model. In [26], a model named PATE-GAN was proposed, which adds formal privacy guarantees by integrating the PATE framework (private aggregation of teacher ensembles) to the GAN method. Differential privacy (DP) ensures that the output of an algorithm does not reveal much about any individual sample in the training data, i.e., the authors use PATE to ensure the discriminator part of the GAN respects DP. The real dataset is partitioned into multiple disjoint subsets, and the discriminator is trained on each subset. After that, the predictions are used to create noisy labels, which are used to train a discriminator, thus creating a differentiable model. The authors tested their model with various datasets across multiple domains, such as fraud detection and medical tasks. The datasets used in their study include low and high dimensions, balanced and imbalanced classes, etc. A novel GAN-based model (CTGAN) specifically designed to generate realistic synthetic tabular data, which is challenging due to mixed data types, non-Gaussian and multimodal continuous features, and imbalanced categorical variables, was introduced in [27]. It addresses these challenges using a mode-specific normalization technique for continuous variables and a conditional generator with training-by-sampling to ensure balanced learning across categories. It outperforms existing GAN-based methods and even Bayesian network models on a comprehensive benchmark of real and simulated datasets. The authors also introduced SDGym, an open-source benchmarking framework for evaluating tabular data synthesizers, demonstrating that CTGAN achieves superior machine learning efficacy and data fidelity in most cases. A combination of seven simulated and eight real-world tabular datasets was used to evaluate synthetic data generation. Simulated datasets are based on Gaussian mixture models and Bayesian networks to allow for controlled testing of model fidelity. In [28], the WCGAN-GP model was used to generate tabular datasets with mixed data types. The WCGAN-GP relies on the Wasserstein distance and the gradient penalty (WGAN-GP). It reduces the occurrence of failure modes that are encountered when using GANs. The WCGAN-GP is very similar to the WGAN-GP, with the only difference being that the discriminator (here called critic) and generator are both conditioned on extra information of class labels. The critic predicts values that are large for real and small for fake samples instead of classifying them as real or fake [28; 29]. While prior studies have successfully applied GAN architectures for synthetic electrical load profile generation, the majority focus on big datasets or apply recurrent models (e.g., RNNs and LSTMs) to capture temporal dependencies. These approaches, although effective for modeling sequential patterns, often require extensive data, are computationally expensive and exhibit instability when trained on small or noisy datasets. Furthermore, RNN-based GANs are inherently complex to train, suffer from vanishing gradients, and offer limited parallelization capabilities, which restrict their practical deployment in real-time or resource-constrained environments. At the same time, the potential of convolutional architectures, particularly 1D convolutional GANs (Conv1D-GANs), remains unresearched in the energy domain. While convolutional layers have proven highly effective in capturing local structure and neighborhood relations in spatial data, their application to load profile generation has not been fully explored. Especially lacking are studies that demonstrate whether Conv1D-GANs can achieve high fidelity, statistical realism, and generalizability when trained on small-scale datasets, such as single-household profiles or industrial profiles with limited samples. In this study, we define small-scale and medium-scale datasets as those containing fewer than 500 and between 500 and 10,000 records, respectively.