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.
REF also presented a study on holiday detection from energy consumption data based on low-resolution smart meter data.
Unsupervised Holiday Detection from Low-resolution Smart Metering Data
ICISSP
Computer Science