Utilities are currently deploying smart electricity meters in millions of 
households worldwide to collect fine-grained electricity consumption data. We 
present an approach to automatically analyzing this data to enable 
personalized and scalable energy efficiency programs for private households. 
In particular, we develop and evaluate a system that uses supervised machine 
learning techniques to automatically estimate specific "characteristics" 
of a household from its electricity consumption. The characteristics are 
related to a household's socio-economic status, its dwelling, or its appliance 
stock. We evaluate our approach by analyzing smart meter data collected from 
4232 households in Ireland at a 30min granularity over a period of 1.5 years. 
Our analysis shows that revealing characteristics from smart meter data is 
feasible, as our method achieves an accuracy of more than 70% over all 
households for many of the characteristics and even exceeds 80% for some of 
the characteristics. The findings are applicable to all smart metering systems 
without making changes to the measurement infrastructure. The inferred 
knowledge paves the way for targeted energy efficiency programs and other 
services that benefit from improved customer insights. On the basis of these 
promising results, the paper discusses the potential for utilities as well as 
policy and privacy implications.