# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 1 Introduction
In this study, the data were divided into time segments, and each segment was divided into time slots. In the follow-up case studies, the time of the day was divided into five segments in the summer and four segments in the winter. A more advanced model based on Markov chain theory is presented in [3]. The training data are analyzed on monthly and hourly bases, and it is implemented using 24 h and 24 h change transition matrices. It showed quite a good performance at generating different types of load profiles, such as domestic, agricultural, and industrial, and was able to maintain both the statistical and probabilistic properties of the original datasets. The authors concluded that the number of states of the transition matrices is an important parameter for generating realistic data and recommended using 20+ states for optimal results. However, the results might be unsatisfactory when a limited amount of training data are available. The second widely used method for generating load profiles is through neural networks (NNs). For example, to generate synthetic load profiles that mimic real-world electricity consumption patterns, [19] used publicly available weather data and load data from neighboring or similar regions, combined with artificial neural networks (ANNs). The challenge of limited training data was addressed by utilizing data from neighboring regions and Bayesian regularization to improve the generalization. Among NNs, generative adversarial networks (GANs) [20; 21] and variational auto-encoders (VAEs) [7; 22] have proven themselves as optimal solutions. Presented for the first time by [PERSON] et al. (2014) [23], the GAN method is characterized by its ability to detect the underlying distributions of the data, thus creating a more realistic dataset. The method is also considered flexible, as it allows for the application of data generation across various domain fields and tasks. The GAN method is preferred among researchers for synthetic data generation when there is not enough data or more diversity among real samples is required [24]. In [1], a data-driven approach for electrical load profile data generation was proposed based on a GAN and data from 156 office buildings with a temporal granularity of 1 h, resulting in 56,957 daily load profiles. The basic GAN algorithm was used with the additional step of clustering. Nineteen clusters were used, each one accounting for 2-9% of the total data, and GAN models were trained for them. The obtained results showed that the GAN captured not only the general trend but also the random variations in the actual loads. Similarly, in [4], an adaptation of the original GAN algorithm was used, called DoppelGANGer, which was optimized for time-series data with high fidelity. A smaller dataset of 129,600 samples was used to gain a better understanding of the model's characteristics and working mechanisms. They further divide the training data into batches of 90 samples per iteration. However, the quality of the generated data was not compared to the original one. A new variation of the GAN method was proposed in [25]. The ERGAN dataset was split into clusters with the K-Means method, and a separate GAN model was trained on every cluster. The authors claimed that this technique allows the framework to capture more precisely the unique characteristics and variabilities of the data. The study is focused on generating synthetic data for residential buildings, and the results showed that the proposed methodology outperformed the selected benchmark models (WGAN, ACGAN, and C-RNN-GAN) in terms of compliance with the original dataset. A significantly different approach was used in [24], where the GAN's ability to produce high-resolution images is investigated. The authors propose an advanced GAN-ACGAN model, which relies on transforming the load profile into load matrices, resembling images. Every row consists of the daily load profiles for one week. Every element in the matrix is related to the adjacent elements like a pixelated image.