Given the vast amounts of data generated by smart meters in smart grids, developing effective compression schemes is essential to address storage and communication challenges. However, the suitability of deep learning models for compressing smart meter data with events characterized by sharp transitions, jumps, and surge peaks from appliance activities remains underexplored. In this paper, we underscore that generalization performance deteriorates substantially when autoencoder approaches fail to preserve the shift equivariance property, indicating that the model overlooks essential features and instead relies on non-generalizable patterns. Furthermore, we observe that autoencoders incur significant reconstruction errors when handling transient periods due to Gibbs-like and staircase artifacts . To address these challenges, we propose an event-preserving autoencoder framework that maintains shift equivariance and is robust to inherent transient load profiles in smart meter data. To this end, it combines a convolution-based architecture with an event-preserving regularization term. Evaluations on public datasets demonstrate that our method outperforms existing autoencoders and a state-of-the-art transformer for time-series data, establishing it as an efficient and reliable solution for compressing smart meter data with events. Furthermore, we analyze how quantization and entropy-constrained loss influence reconstruction quality and compression ratio performance, providing insights into the trade-offs in practical deployments. • Shift equivariance ensures superior generalization for smart meter compression. • Total variation effectively mitigates Gibbs-like artifacts in transient periods. • Proposed method achieves performance comparable to bzip2 and LZMA in smart grid tasks • Codebook-based quantization achieves better performance under entropy constraint.
Seo et al. (Thu,) studied this question.