## 1 Introduction
The goal is to implement an intelligent decision support system based on reinforcement learning that analyzes user behavior and environment data to predict energy consumption. Although this approach shows potential for significant cost reduction and efficiency increase in managing energy consumption in the domestic sector, it also has some problematic aspects, such as difficulties in identifying similar behavior patterns between different days, as well as the need to improve the system to work in real conditions with more complex sensor data. A stochastic model for managing electricity consumption that reflects the variability of consumers' social activities is described in [31]. The model uses Markov chains with random transition matrices to model missing data. Consumers' daily activities are classified into four basic states: \"away\", \"sleeping\", \"home with high consumption\", and \"home with low consumption\". The transition matrices are calculated from time-use data but include stochastic elements to fill in missing or limited data and aggregate the states. The maximum entropy approach is used to define the probabilities of the transition matrices. In [29], it is shown that solar states are interdependent and, therefore, can be modeled with Markov chains. In particular, a multi-segment Markov chain was used, in which the transition probabilities from one solar state to another depended on the moment of time. To capture this dependence, the time intervals were divided into segments, and for each one, a transition matrix was defined based on real data. The study showed promising results;however, the number of states was not calibrated, and the relationship between the number of time segments and solar radiation was not investigated. The analysis of the probability of different events occurring in the renewable sector with Markov chains has also been studied by various authors. In [32], it is shown that the probability for prolonged (10 days) periods of low energy output from photovoltaic installations in Ruse (Bulgaria) varies between 0% and 20% for the different months of the year. Another study for Mogilishte (Bulgaria) demonstrated that the probability of wind turbines' power being below 20% of their rated value for 10 consecutive days varies between 0% and 70% for the different months of the year [33]. Such information could be of crucial importance when designing autonomous installations with renewable energy sources. It shows that the probabilistic information is as important as the statistical information for the energy sector. The analysis of previous studies shows that two main approaches exist for synthetic data generation--the application of deep GANs and Markov chains. An obvious research gap is that the probabilistic characteristics of the data generation solutions were neither investigated nor optimized. However, when it comes to load profiles and digital energy systems, it is important to maintain the occurrence probabilities of different random events, such as extreme peaks, prolonged high or low consumption, etc. Therefore, the synthetic data for energy applications should keep both the statistical and probabilistic characteristics of the original time series, especially when renewable energy is involved. This study aimed to develop a methodology for generating synthetic load profiles that retain the statistical and probabilistic characteristics of the actual experimental data. To achieve this, evaluation criteria and general application recommendations will be defined. The proposed approach will enable the generation of realistic energy demand profiles that reflect the actual consumption trends and the probabilities for the occurrence of different random events, which is the main contribution of this study. Furthermore, the paper includes several case studies to demonstrate the application of the methodology and to illustrate its potential applications in the field of energy management.