The effect of data granularity on load data compression
First published in
Number in series
Energy Informatics. EI 2015. Lecture Notes in Computer Science (9424): 69-80 (2015)
Contribution to Conference
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.