Resumable Load Data Compression in Smart Grids

We propose a compression approach for load profile data, which addresses practical requirements of smart metering. By providing linear time complexity with respect to the input data size, our compression approach is suitable for low-complexity encoding and decoding for storage and transmission of load profile data in smart grids. Furthermore, it allows for resumability with very low overhead on error-prone transmission lines, which is an important feature not available for standard time series compression schemes. In terms of compression efficiency, our approach outperforms transmission encodings that are currently used for electricity metering by an order of magnitude.

[1]  Gregory Murphy,et al.  Embedded zerotree wavelet based data compression for smart grid , 2013, 2013 IEEE Industry Applications Society Annual Meeting.

[2]  M. Etezadi-Amoli,et al.  Smart meter based short-term load forecasting for residential customers , 2011, 2011 North American Power Symposium.

[3]  M. Ringwelski,et al.  The Hitchhiker's guide to choosing the compression algorithm for your smart meter data , 2012, 2012 IEEE International Energy Conference and Exhibition (ENERGYCON).

[4]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[5]  Jack G. Ganssle,et al.  Art of Designing Embedded Systems , 1999 .

[6]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[7]  J. Kraus,et al.  Optimal data compression techniques for Smart Grid and power quality trend data , 2012, 2012 IEEE 15th International Conference on Harmonics and Quality of Power.

[8]  Ian H. Witten,et al.  Arithmetic coding for data compression , 1987, CACM.

[9]  Glen G. Langdon,et al.  An Overview of the Basic Principles of the Q-Coder Adaptive Binary Arithmetic Coder , 1988, IBM J. Res. Dev..

[10]  Ralf Steinmetz,et al.  On the accuracy of appliance identification based on distributed load metering data , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[11]  Cong Liu,et al.  A Wavelet-Based Data Compression Technique for Smart Grid , 2011, IEEE Transactions on Smart Grid.

[12]  Andreas Uhl,et al.  Region of interest signalling for encrypted JPEG images , 2013, IH&MMSec '13.

[13]  Takehiro Moriya,et al.  The MPEG-4 Audio Lossless Coding (ALS) Standard - Technology and Applications , 2005 .

[14]  Anno Accademico,et al.  Smart Grid Communications: Overview of research challenges, solutions and standardization activities , 2013 .

[15]  Heiko Schwarz,et al.  Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[16]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[17]  Patrick D. McDaniel,et al.  Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.

[18]  Vipul Gupta,et al.  Energy analysis of public-key cryptography for wireless sensor networks , 2005, Third IEEE International Conference on Pervasive Computing and Communications.