Simulating Intraday Electricity Consumption with ForGAN

Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generative adversarial network (GAN), to German electricity consumption data. Electricity consumption time series have been selected due to their typical combination of (non-linear) seasonal behavior on different time scales and of local random effects. The primary objective is to demonstrate that ForGAN is able to capture such complicated seasonal figures and to generate data with the correct underlying conditional distribution without data preparation, such as de-seasonalization. In particular, ForGAN does so without assuming an underlying model for the evolution of the time series and is purely data-based. The training and validation procedures are described in great detail. Specifically, a long iteration process of the interplay between the generator and discriminator is required to obtain convergence of the parameters that determine the conditional distribution from which additional artificial data can be generated. Additionally, extensive quality assessments of the generated data are conducted by looking at histograms, auto-correlation structures, and further features comparing the real and the generated data. As a result, the generated data match the conditional distribution of the next consumption value of the training data well. Thus, the trained generator of ForGAN can be used to simulate additional time series of German electricity consumption. This can be seen as a kind of proof for the applicabilty of ForGAN. Through a detailed descriptions of the necessary steps of training and validation procedures, a detailed quality check before the actual use of the simulated data, and by providing the intuition and mathematical background behind ForGAN, this contribution aims to demystify the application of GANs to motivate both theorists and researchers in applied sciences to use them for data generation in similar applications. The proposed framework has laid out a plan for doing so.