In this paper, we propose a novel lossless data compression framework, called Stochastic Pattern Discrete Flow (SPDFlow), designed for lightweight IoT applications. SPDFlow integrates normalizing flow models with range Asymmetric Numeral Systems (rANS) to efficiently compress high-frequency and stochastic time-series smart metering data. Building on advances in invertible flow-based distribution mapping, we design an online compression scheme optimized for electricity usage profiles sampled from tens of milliseconds to several seconds. The proposed linear and lightweight architecture enables real-time deployment on resource-constrained edge devices while maintaining robust modeling capability under unpredictable, event-driven peaks. A customized discretization strategy allows efficient encoding of wide-ranging measurement values. Comprehensive theoretical analysis and experiments across diverse datasets verify SPDFlow’s superior compression performance compared with both traditional and recent learning-based methods achieving up to 31% data volume reduction under short update intervals. Furthermore, real-world deployment on Raspberry Pi devices confirms its practical feasibility. To the best of our knowledge, this is the first work to explore lossless compression of time-series load profiles using normalizing flows.

Sam Jin Pharmaceutical, Seoul, Korea and Chong Kun Dang Pharmaceutical, Seoul, Korea.