Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 14; p. 5331
• Main Authors: Ma, Zhoujun, Zhu, Hong, He, Zhuohao, Lu, Yue, Song, Fuyuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16.07.2022; MDPI
• Subjects: Algorithms; arithmetic coding; Communication; Data compression; Decomposition; Deep learning; Efficiency; Electricity distribution; Long Short-Term Memory; Methods; Neural networks; Performance evaluation; Privacy; Probability distribution; smart grid; transformer; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22145331

Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.