Tri-Compress: A Cascaded Data Compression Framework for Smart Electricity Distribution Systems

TL;DR

This paper introduces Tri-Compress, a three-stage cascaded data compression framework combining lossy and lossless techniques to significantly improve data compression efficiency for smart electricity distribution systems.

Contribution

It presents a novel cascaded compression approach that outperforms traditional methods by integrating SVD, normalization, and sparsity encoding for better data reduction.

Findings

Achieves up to 15% higher compression ratio than SVD in sparse datasets.

Achieves up to 28% higher compression ratio in large, non-sparse datasets.

Maintains acceptable Mean Absolute Error during high compression.

Abstract

Modern smart distribution system requires storage, transmission and processing of big data generated by sensors installed in electric meters. On one hand, this data is essentially required for intelligent decision making by smart grid but on the other hand storage, transmission and processing of that huge amount of data is also a challenge. Present approaches to compress this information have only relied on the traditional matrix decomposition techniques benefitting from low number of principal components to represent the entire data. This paper proposes a cascaded data compression technique that blends three different methods in order to achieve high compression rate for efficient storage and transmission. In the first and second stages, two lossy data compression techniques are used, namely Singular Value Decomposition (SVD) and Normalization; Third stage achieves further compression by using the technique of Sparsity Encoding (SE) which is a lossless compression technique but only having appreciable benefits for sparse data sets. Our simulation results show that the combined use of the 3 techniques achieves data compression ratio to be 15% higher than state of the art SVD for small, sparse datasets and up to 28% higher in large, non-sparse datasets with acceptable Mean Absolute Error (MAE).