Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data

Andre Catarino, Rui Melo, Luís Cruz, Rui Abreu

Keywords: Dynamic Time-of-Use Tariffs, Smart Grid, Energy Consumption Pattern Classification, Synthetic Data Augmentation, Privacy-Preserving Machine Learning

TL;DR: This paper evaluates how different generative models balance privacy and utility when creating synthetic smart-grid data for predicting household suitability to dynamic electricity tariffs.

Abstract: The widespread adoption of dynamic Time-of-Use (dToU) electricity tariffs requires accurately identifying households that would benefit from such pricing structures. However, the use of real consumption data poses serious privacy concerns, motivating the adoption of synthetic alternatives. In this study, we conduct a comparative evaluation of four synthetic data generation methods, Wasserstein–GP Generative Adversarial Networks (WGAN), Conditional Tabular GAN (CTGAN), Diffusion Models, and Gaussian noise augmentation, under different synthetic regimes. We assess classification utility, distribution fidelity, and privacy leakage. Our results show that architectural design plays a key role: diffusion models achieve the highest utility (macro-F1 up to 88.2%), while CTGAN provide the strongest resistance to reconstruction attacks. These findings highlight the potential of structured generative models for developing privacy-preserving, data-driven energy systems.

Submission Number: 11