Tri-Compress:A Cascaded Data Compression Framework For Smart Electricity Distribution Systems
Abstract
Modern smart distribution system requires storage, transmission, and processing of big data genÂerated 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 pro cessing 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 a low number of principal components to represent the entire data. This paper proposes a cascad ed data compression technique "Tri-Compress" that blends three different methods in order to achieve a high compression rate for efficient storage and transmission. I n the first and second stag es, two lossy data compression techniq ues are used, namely Singular Value Decomposition (SVD) and Normalization; the Third stage achieves further compression by using the technique of Sparsity Encoding (SE) which is a lossless compression techniq ue 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 1S% higher than state of the art SVDfor small, sparse datasets and up to 28% higher in large,non-sparse datasets with acceptable Mean Absolute Error (MAE).
KEYWORDSSingular Value Decomposition, Sparse Matrix Representation, Smart Grid, Data Compression, Big Data
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