TY - GEN
T1 - The Effect of Data Granularity on Load Data Compression
AU - Unterweger, A.
AU - Engel, D.
AU - Ringwelski, M.
N1 - Conference code: 160649
Cited By :12
Export Date: 14 December 2023
Correspondence Address: Unterweger, A.; Josef Ressel Center for User-Centric Smart Grid Privacy, Urstein Süd 1, Austria; email: [email protected]
Funding details: Austrian Federal Ministry of Economy, Family and Youth, BMWFJ
Funding text 1: The financial support by the Austrian Federal Ministry of Economy, Family and Youth and the Austrian National Foundation for Research, Technology and Development is gratefully acknowledged.
References: (1986) Coded Character Sets - 7-Bit American National Standard Code for Information Interchange (7-Bit ASCII); (2000) Distribution Automation Using Distribution Line Carrier Systems - Part 6: A-XDR Encoding Rule; (2002) Electricity Metering - Data Exchange for Meter Reading, Tariff and Load Control - Part 21: Direct Local Data Exchange; Efthymiou, C., Kalogridis, G., Smart grid privacy via anonymization of smart metering data (2010) Proceedings of First IEEE International Conference on Smart Grid Communications, pp. 238-243. , Gaithersburg, Maryland, USA; Eibl, G., Engel, D., Influence of data granularity on smart meter privacy (2015) IEEE Trans. Smart Grid, 6 (2), pp. 930-939; Engel, D., Wavelet-based load profile representation for smart meter privacy (2013) Proceedings of IEEE PES Innovative Smart Grid Technologies (ISGT 2013), pp. 1-6. , http://dx.doi.org/10.1109/ISGT.2013.6497835, Washington, D.C., USA; Cost-benefit analyses & state of play of smart metering deployment in the EU-27 (2014) Technical Report, European Commission Report, , http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52014SC0189&from=EN; Khan, J., Bhuiyan, S., Murphy, G., Arline, M., Embedded zerotree wavelet based data compression for smart grid (2013) 2013 IEEE Industry Applications Society Annual Meeting, pp. 1-8; Kolter, J., Johnson, M.J., Redd: A public data set for energy disaggregation research (2011) Workshop on Data Mining Applications in Sustainability (SIGKDD), pp. 1-6; Ning, J., Wang, J., Gao, W., Liu, C., A wavelet-based data compression technique for smart grid (2011) IEEE Trans. Smart Grid, 2 (1), pp. 212-218; Ringwelski, M., Renner, C., Reinhardt, A., Weigel, A., Turau, V., The Hitchhiker’s guide to choosing the compression algorithm for your smart meter data (2012) 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), pp. 935-940; Sankar, L., Rajagopalan, S.R., Mohajer, S., Poor, H.V., Smart meter privacy: A theoretical framework (2013) IEEE Trans. Smart Grid, 4 (2), pp. 837-846; Unterweger, A., Engel, D., Resumable load data compression in smart grids (2015) IEEE Trans. Smart Grid, 6 (2), pp. 919-929. , http://dx.doi.org/10.1109/TSG.2014.2364686
PY - 2015
Y1 - 2015
N2 - A vast volume of data is generated through smart metering. Suitable compression mechanisms for this kind of data are highly desirable to better utilize low-bandwidth links and to save costs and energy. To date, the important factor of data resolution has been neglected in the compression of smart meter data. In this paper, we review and evaluate compression methods for smart metering in the context of different resolutions. We show that state-of-the-art compression methods are well suited for high resolution, but not for low resolution data. Furthermore, we elaborate on the compression performance differences between appliance-level and household-level load data. We conclude that the latter are practically incompressible at most resolutions. © Springer International Publishing Switzerland 2015.
AB - A vast volume of data is generated through smart metering. Suitable compression mechanisms for this kind of data are highly desirable to better utilize low-bandwidth links and to save costs and energy. To date, the important factor of data resolution has been neglected in the compression of smart meter data. In this paper, we review and evaluate compression methods for smart metering in the context of different resolutions. We show that state-of-the-art compression methods are well suited for high resolution, but not for low resolution data. Furthermore, we elaborate on the compression performance differences between appliance-level and household-level load data. We conclude that the latter are practically incompressible at most resolutions. © Springer International Publishing Switzerland 2015.
KW - Bandwidth compression
KW - Electric measuring instruments
KW - Information science
KW - Smart meters
KW - Compression mechanism
KW - Compression methods
KW - Compression performance
KW - Data granularity
KW - Data resolutions
KW - Different resolutions
KW - Low-bandwidth links
KW - State of the art
KW - Data compression
U2 - 10.1007/978-3-319-25876-8_7
DO - 10.1007/978-3-319-25876-8_7
M3 - Conference contribution
SN - 978-3-319-25875-1
VL - 9424
SP - 69
EP - 80
BT - Energy Informatics
PB - Springer International Publishing
T2 - 4th D-A-CH Conference on Energy Informatics, EI 2015
Y2 - 12 November 2015 through 13 November 2015
ER -
