Privacy-Preserving Smart Metering through Data Synthesis with Differential Privacy

Smart meters (SMs) are a cornerstone of the smart grid, generating high-resolution electricity consumption data that enables load forecasting, grid optimisation, and advanced analytics. However, the fine-grained nature of this data introduces significant privacy risks, potentially revealing sensitive information such as user identities, daily routines, and occupancy patterns. Existing privacy-preserving techniques often face challenges in balancing data utility with privacy protection. To address these limitations, we propose a novel method for generating Differentially Private (DP) synthetic SM data using a Transformer-based Conditional GAN (CGAN). Our approach ensures strong privacy guarantees while preserving the utility necessary for critical downstream tasks such as load forecasting and energy optimisation. Comprehensive experiments validate the effectiveness of our method, demonstrating up to 82% stronger privacy protection compared to standard DP-only mechanisms. It also achieves a 35% improvement in load forecasting utility over synthetic data generated by prior Transformer-based CGAN models and 66.7% higher utility compared to DP-synthetic data produced by existing DP-GAN methods. In addition, our approach increases robustness against adversarial attacks by 28% relative to models trained on real SM data. These results highlight the potential of our approach to support privacy-preserving analytics in the smart grid without compromising performance.