TY - GEN
T1 - Insights into Unsupervised Holiday Detection from Low-Resolution Smart Metering Data
AU - Eibl, G.
AU - Burkhart, S.
AU - Engel, D.
N1 - Conference code: 228459
Cited By :1
Export Date: 14 December 2023
Correspondence Address: Eibl, G.; Center for Secure Energy Informatics, Urstein Süd 1, Austria; email: [email protected]
Funding text 1: Acknowledgement. The financial support by the Federal State of Salzburg is gratefully acknowledged. Furthermore, the authors would like to thank the Energieinstitut at the Johannes Kepler University Linz for providing the data set.
References: Becker, V., Kleiminger, W., Exploring zero-training algorithms for occupancy detection based on smart meter measurements (2018) Comput. Sci. Res. Dev, 33 (1-2), pp. 25-36. , https://doi.org/10.1007/s00450-017-0344-9; Chen, D., Barker, S., Subbaswamy, A., Irwin, D., Shenoy, P., Non-intrusive occupancy monitoring using smart meters (2013) Proceedings of the 5Th ACM Workshop on Embedded Systems for Energy-Efficient Buildings-Buildsys 2013, pp. 1-8. , https://doi.org/10.1145/2528282.2528294; Eibl, G., Burkhart, S., Engel, D., Unsupervised holiday detection from Low-resolution smart metering data (2018) 2018 Proceedings of the 4Th International Conference on Information Systems Security and Privacy, ICISSP, pp. 477-486. , https://doi.org/10.5220/0006719704770486; Hart, G.W., Nonintrusive appliance load monitoring (1992) Proc. IEEE, 80 (12), pp. 1870-1891; Jin, M., Jia, R., Spanos, C., Virtual occupancy sensing: Using smart meters to indicate your presence (2017) IEEE Trans. Mob. Comput, 16 (11), pp. 3264-3277. , http://ieeexplore.ieee.org/document/7882676/; Kavousian, A., Rajagopal, R., Fischer, M., Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior (2013) Energy, 55, pp. 184-194. , https://doi.org/10.1016/j.energy.2013.03.086; Kim, H., Marwah, M., Arlitt, M.F., Lyon, G., Han, J., Unsupervised disaggregation of low frequency power measurements (2011) The 11Th SIAM International Conference on Data Mining, pp. 747-758. , pp; Kleiminger, W., Beckel, C., Santini, S., Household occupancy monitoring using electricity meters (2015) Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 975-986. , https://doi.org/10.1145/2750858.2807538, pp; Kleiminger, W., Beckel, C., Staake, T., Santini, S., http://dl.acm.org/citation.cfm?doid=2528282.2528295; Lisovich, M.A., Wicker, S.B., Privacy concerns in upcoming residential and commercial demand-response systems (2008) Clemson Power Systems Conference. IEEE; Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S., Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey (2012) Sensors (Switzerland), 12 (12), pp. 16838-16866. , https://doi.org/10.3390/s121216838
PY - 2019
Y1 - 2019
N2 - Recently, first methods for holiday detection from unsupervised low-resolution smart metering data have been presented. However, due to the unsupervised nature of the problem, previous work only applied the algorithms on a few typical cases and lacks a systematic validation. This paper systematically validates the existing algorithm by visual inspection and shows that numerous cases exist, where implicit assumptions are not met and the methods fail. Moreover, it proposes a new, very simple rule-based method which is in principle able to overcome these problems. This method should be seen as a first step towards improvement, since it is not automated and needs a moderate amount of human intervention for each household. © 2019, Springer Nature Switzerland AG.
AB - Recently, first methods for holiday detection from unsupervised low-resolution smart metering data have been presented. However, due to the unsupervised nature of the problem, previous work only applied the algorithms on a few typical cases and lacks a systematic validation. This paper systematically validates the existing algorithm by visual inspection and shows that numerous cases exist, where implicit assumptions are not met and the methods fail. Moreover, it proposes a new, very simple rule-based method which is in principle able to overcome these problems. This method should be seen as a first step towards improvement, since it is not automated and needs a moderate amount of human intervention for each household. © 2019, Springer Nature Switzerland AG.
KW - Privacy
KW - Smart grids
KW - Smart metering
KW - Data privacy
KW - Information systems
KW - Information use
KW - Holiday detections
KW - Human intervention
KW - Low resolution
KW - Rule-based method
KW - Smart grid
KW - Visual inspection
KW - Electric measuring instruments
U2 - 10.1007/978-3-030-25109-3_15
DO - 10.1007/978-3-030-25109-3_15
M3 - Conference contribution
SN - 978-3-030-25108-6
VL - 977
T3 - Communications in Computer and Information Science
SP - 281
EP - 302
BT - Information Systems Security and Privacy
PB - Springer Nature
T2 - 4th International Conference on Information Systems Security and Privacy, ICISSP 2018
Y2 - 22 January 2018 through 24 January 2018
ER -
SDG 7 Affordable and Clean Energy