# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 1 Introduction  
The variation in a building's, industry's, or sector's electrical energy consumption over time is referred to as \"electrical load profile\" [1] or as \"load shape\", which is the curve that represents the load as a function of time [2]. Regardless of the used term, it is known that various factors influence the load profile. For example, the consumption of domestic consumers depends on the time of day, whether it is a weekend or a workday, the meteorological conditions, and other factors. Similarly, for agricultural consumers, the load profile is dependent on factors such as the agro-technological process schedule and the meteorological conditions [3]. Collecting data on building electrical load profiles, as well as on different sectors and scenarios, could be an expensive and time-consuming task [1]. This is further complicated when factoring in the privacy concerns, mentioned by multiple authors [4, 5]. One solutionto address privacy issues is the generation of synthetic data that do not directly correspond to the real pattern; however, its statistical and/or probabilistic characteristics are maintained [6,7]. The main source of real load profile data is smart meters, which record the electrical energy consumption and send the information to a server for further processing and storage [8]. Nowadays, using such meters is becoming increasingly popular, as this provides easy access to consumption datasets and is the backbone of digital energy systems. The two main studies on the load profile can be categorized as descriptive, in which the load profile is described by its characteristics, and generative, in which the aim is to generate the load profile as realistically as possible [1]. Two approaches exist for generating building load profiles, as follows: white-box approach and black-box approach. The white-box approach relies on information about the physics of the building, predicted occupants' behavior, and demands [9]; appliance schedule [10]; etc. Based on these data, energy models are created for the simulation of the energy consumption. On the contrary, the logic of the decision-making process that lies behind a black-box approach remains hidden from the user and is not easily accessible and interpretable [11]. According to [12], the generated synthetic data should have the following three properties:  
* Fidelity--the synthetic data should have the same or similar data distribution as the original data;
* Flexibility--the models, generating the data, should allow for the generation of data of a particular class;
* Privacy--the generation of the data should be anonymized while keeping its integrity and distribution. Furthermore, according to [3], the synthetic data should keep not only the statistical but also the probabilistic characteristics of the original dataset. Different methods exist for generating black-box load profiles, with two of them being the most widely used. The first one is based on Markov chain theory and relies on a random generator applied to one or more transition matrices [13,14]. For example, in [15], a simple Markov-chain mixture-distribution model was proposed for very short-term (half an hour) forecasting, which can also be used for synthetic data generation when combined with a random number generator. A slightly more complex approach was presented in [16]. The proposed model includes preliminary hierarchical clustering of the data, followed by the generation of transition matrices and random walks using a random generator. In [17], a 24 \(\times\) 24 transition matrix was used to model domestic load profiles. In this study, only the first record was chosen randomly, and the next samples were chosen based on the highest probability of occurrence. The authors stated that certain statistical properties of the synthetic data were satisfactory; however, the temporal properties did not compare well with the original dataset. In [18], first-order Markov chains were used for generating synthetic solar energy data with application in smart grids.