## 1 Introduction  
The increasing integration of renewable energy sources into the modern power grids has highlighted the need for accurate and reliable energy load profiles. These profiles are crucial for grid management, energy forecasting, and optimizing demand-response strategies. However, obtaining real-world load data is often challenging due to privacy concerns, measurement limitations, and data availability [1]. As a result, a growing interest in developing synthetic load profiles that can mimic the statistical characteristics and probabilistic variations of actual consumption patterns is observed [2]. Solutions for creating models to generate synthetic data [3] are widely discussed in the literature [4, 5, 6]. Load profiles have the following features: they are data that reflect events or quantities over time and, therefore, carry a timestamp; they are a series of ordered pairs of values or states at a certain point in time, which are commonly fixed. Another characteristic of this series is that it is usually subject to certain time correlations and regular characteristics--there is some periodicity, trends, or seasonality. The characteristics can be extracted, processed, analyzed, and used to generate synthetic load profiles using various approaches and algorithms for time series analysis, signal processing, or machine learning (ML). In [7], 417 synthetic data generation models from the last decade are reviewed, and their functionalities and improvements are described. According to the authors, there is a trend towards increased performance and complexity of models, with neural-network-based models dominating. When a model is chosen, it is important to take into account that it is not possible to use the same approaches to synthetic data generation for different subject areas and that the time and cost of training ML algorithms should not be neglected. Systematic reviews of the application of machine learning models to synthetic data generation were also conducted in [8; 9]. They cover synthetic data generation for application in various subject areas (computer vision, natural language processing, speech, different business aspects, healthcare, and others) and identify the challenges and opportunities of ML in data generation. Long-term energy system analysis has been the subject of extensive research, primarily aimed at estimating future energy demand to guide the development of appropriate energy generation and distribution infrastructure [4]. With the advent of renewable energy, the forecasting requirement has also changed. Even in long-term applications, the time step has been reduced to reflect the rapid changes in production in short-term intervals. This change has led to the development of hybrid forecasting models that combine long-term planning with high-resolution temporal data to improve system flexibility and reliability. Due to these dynamics, using naturally collected data is sometimes insufficient, and it is often necessary to add synthetically generated data [10]. Another reason for this is the lack of high temporal resolution in the data (to accumulate real data, monitoring over a very long period is necessary) for electricity consumption, which makes it difficult to generate cost-effective, realistic forecasts and optimize the consumption. Furthermore, often privacy issues arise with the data used, which might be overcome by relying on synthetic data instead [11]. When it comes to data generation, the terms data forecasting and synthetic data generation are similar yet different. Forecasting is aimed at generating data that are as close as possible to the actual data, which has not yet occurred. Depending on the usage scenarios, the forecasts are commonly divided into very short-, short-, medium-, and long-term [12]. Considering the above, it is implemented on a probabilistic basis, i.e., algorithms are used to determine the scenario that has the highest occurrence probability.