Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data

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 do...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 22; H. 14; S. 5331
Hauptverfasser: Ma, Zhoujun, Zhu, Hong, He, Zhuohao, Lu, Yue, Song, Fuyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 16.07.2022
MDPI
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22145331