## 1 Introduction
Synthetic data generation could also be divided within the above-mentioned categories, depending on the specific application; however, in this case, the generated data must correspond to the real one only in a statistical and/or probabilistic way [13]. The synthetic data relies on random generators, i.e., these algorithms will not always \"choose\" the scenario with the highest probability of occurrence, unlike the forecasting ones. ML algorithms are commonly used to extract information and knowledge from big data, but they can also be used to generate synthetic data for reasons, such as insufficient volume, gaps, availability, or confidentiality issues. In [14], the authors propose an approach for generating synthetic data for classification and regression that uses the K-nearest neighbor (KNN) model. The general framework of this approach includes the following main steps: from the available data, processed samples are prepared, which are used to train a KNN model to capture the correlations between the features. ML algorithms have one major advantage in generating synthetic data--they can identify complex dependencies in the data and produce synthetic data sets that preserve the underlying statistical relationships and distributional characteristics of the original dataset. According to [15], the most popular ML approaches are generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs). VAEs use a probabilistic approach to generate synthetic data. Unlike traditional autoencoders, VAEs introduce a stochastic component by encoding data into a distribution rather than a fixed point, enabling the generation of diverse and realistic samples [16]. VAEs are particularly useful in domains where high-dimensional and structured data, such as images, time series, and text, should be synthesized. In synthetic data generation, VAEs can produce new samples that resemble real-world distributions [17], making them valuable for augmenting datasets, addressing class imbalances, and enhancing privacy in machine learning applications by generating data that do not directly correspond to real entities. LLMs are powerful tools for generating synthetic data by leveraging their ability to learn and generalize from vast amounts of text. Those models can produce realistic, coherent, and contextually appropriate text. Their capacity to generate diverse samples helps mitigate biases, improve model robustness, and create training data for low-resource languages or specialized domains. Additionally, LLMs can be fine-tuned to generate synthetic data tailored to specific requirements, such as anonymized text for privacy-preserving applications or domain-specific problems [18]. LLMs show high efficiency in producing synthetic text data but require significant computational resources. The analysis in [19] identifies thirteen methods applicable for generating synthetic energy time series, which vary in frequency of occurrence. According to the authors, Markov models, weighted random number generator (wRNG) methods, and GANs are used in more than half of the articles. Although GANs were developed with the main idea of image processing and computer vision, many researchers have shown in their publications [20; 21; 22; 23] that they are also successfully applicable in synthetic data generation for the energy sector. Various modifications of GANs are an effective method for generating synthetic energy data that preserve the characteristics of real data. This provides significant opportunities for protecting privacy, reducing data collection costs, and improving research in energy systems. In [24], the authors proposed a data-driven application of deep GANs. They do not use synthetic data generation through system dynamics modeling but are based on learning the conditional probability distribution of the underlying features. The evaluation of the synthetically generated data was based on measuring the maximum mean discrepancy between real and synthetic data sets and showed strong convergence.