Abstract
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.
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Acknowledgements
The authors would like to thank Günther Eibl for his help in visualizing the compression ratio results. They would also like to thank their partner Salzburg AG for providing additional real-world load data.
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.
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Unterweger, A., Engel, D., Ringwelski, M. (2015). The Effect of Data Granularity on Load Data Compression. In: Gottwalt, S., König, L., Schmeck, H. (eds) Energy Informatics. EI 2015. Lecture Notes in Computer Science(), vol 9424. Springer, Cham. https://doi.org/10.1007/978-3-319-25876-8_7
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DOI: https://doi.org/10.1007/978-3-319-25876-8_7
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