Name
Abstract | ||
|---|---|---|
The implementation of electricity smart meters has raised a number of privacy concerns, related to all sorts of information about the nature of the residents that could be inferred from readings of the power consumption. In this paper we attempt to classify households according to different classes, ranging from the presence of kids and of specific appliances to the employment status and education level of the residents. We apply a wide range of features and classification methods and measure the achievable accuracy. It is shown that, at a time resolution of 30 minutes, only a few of the investigated problems give a satisfactorily accuracy, while most of them would require a higher sampling frequency that is not practical for smart meters. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | household appliances,power consumption,data privacy,resident education level,home automation,pattern classification,household classification,smart meters,low-resolution smart meter data,sampling frequency,electricity smart meter implementation,power consumption readings,residential household information mining,data mining,sampling methods,privacy concerns,domestic appliances,resident employment status,classification methods |
Field | DocType | ISSN |
Data mining,Metre,Computer science,Sampling (signal processing),Home automation,Ranging,Artificial intelligence,Information privacy,Computer vision,Electricity,Sampling (statistics),Smart meter,Database | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 5 | 1.01 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Fusco | 1 | 6 | 2.08 |
| Michael Wurst | 2 | 5 | 1.35 |
| Ji Won Yoon | 3 | 112 | 23.94 |