Model-Free Lossless Data Compression for Real-Time Low-Latency Transmission in Smart Grids
Lei Yan, Jiayu Han, Runnan Xu, Zuyi Li
Abstract
This article proposes a model-free lossless data compression method for time series in smart grids (SGs), namely, Lossless Coding considering Precision (LCP) method. The LCP method encodes the current datapoint only using the immediate previous datapoint by differential coding, XOR coding, and variable length coding and transmits the encoded data once generated. It does not use the dynamics (e.g., many previous datapoints) or prior knowledge (e.g., mathematical models) of the time series. It considers the patterns, potential applications, and associated precision to preprocess the time series and especially suits high-resolution time series with long steady periods. The LCP method features low-latency and generalizability which enables real-time data communication for different time-critical tasks. Sub-metered load profiles in REDD dataset, high-resolution LIFTED dataset, AMPds dataset and PMU dataset are used to evaluate the performance of the LCP method. The results show that the LCP method demonstrates high compression ratio, low latency, and low complexity compared to state-of-the-art Resumable Data Compression (RDC) method, DEFLATE based on LZ77 & Huffman coding, and Lempel-Ziv-Markov Chain Algorithm (LZMA).