Capturing multiscale temporal dynamics in synthetic residential load profiles through Generative Adversarial Networks (GANs)

High-resolution residential smart meter data play a pivotal role in numerous applications, ranging from assessing hosting capacity in low-voltage grids to evaluating the economic viability of household-level solutions such as the integration of photovoltaic (PV) installations with battery energy storage systems (BESS). However, privacy concerns often restrict access to large-scale residential smart meter datasets, leaving only synthetic load profiles available to the public, which generally only represent averages of individual data. In response to this challenge, this paper proposes an innovative approach leveraging Generative Adversarial Networks (GANs) to generate synthetic residential load profiles. Our method operates in two distinct phases: First, we employ the DoppelGANger (DGAN) architecture to construct annual time series of daily consumption values. DGAN has been selected due to its demonstrated capacity for capturing interday and seasonal dynamics. Second, a wavelet-based decomposition-recombination technique is employed to create stochastic daily profiles with realistic intraday variations and peak demand behavior. Our DGAN-based approach demonstrates remarkable effectiveness, as evidenced by evaluation against a real-world dataset through a series of qualitative microbenchmarks. Moreover, the practical utility of the two-step methodology is explored in three downstream applications: (i) the integration of PV systems, (ii) PV-BESS systems increasing PV self-consumption, and (iii) PV-BESS systems boosting PV self-consumption while performing peak shaving. Our methodology is shown to accurately capture and model the intricate complexities inherent in real smart meter data.