Unsupervised Holiday Detection from Low-resolution Smart Metering Data

Günther Eibl, Sebastian Burkhart, Dominik Engel

Abstract

The planned Smart Meter rollout at a large scale has raised privacy concern. In this work for the first time holiday detection from smart metering data is presented. Although holiday detection may seem easier than occupancy detection, it is shown that occupancy detection methods must at least be adapted when used for holiday detection. A new, unsupervised method for holiday detection that applies classification algorithms on a suitable re-formulation of the problem is presented. Several algorithms were applied to a big, realistic smart metering dataset that – compared to existing datasets for occupancy detection – is unique in terms of number of households (869) and measurement duration (>1 year) and has a realistic low time resolution of 15 minutes. This allows for more realistic checks of seemingly plausible but unconfirmed assumptions. This work is merely a first starting point for further research in this area with more research questions raised than answered. While the results of the algorithms look plausible in a visual analysis, testing for data with ground truth is most importantly needed.

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Paper Citation


in Harvard Style

Eibl G., Burkhart S. and Engel D. (2018). Unsupervised Holiday Detection from Low-resolution Smart Metering Data.In Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-282-0, pages 477-486. DOI: 10.5220/0006719704770486


in Bibtex Style

@conference{icissp18,
author={Günther Eibl and Sebastian Burkhart and Dominik Engel},
title={Unsupervised Holiday Detection from Low-resolution Smart Metering Data},
booktitle={Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2018},
pages={477-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006719704770486},
isbn={978-989-758-282-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Unsupervised Holiday Detection from Low-resolution Smart Metering Data
SN - 978-989-758-282-0
AU - Eibl G.
AU - Burkhart S.
AU - Engel D.
PY - 2018
SP - 477
EP - 486
DO - 10.5220/0006719704770486