Utilities increasingly utilize knowledge on their customer's household 
characteristics in their energy efficiency programs. Examples of such 
characteristics are the number of persons per household, their employment 
status, or the type of dwelling they live in. This information allows them to 
considerably improve the quality of saving advice, increase participation 
rates and the specificity of behavioral interventions, and ultimately leads to 
larger savings and higher customer retention. We investigate the possibility 
to automatically infer such characteristics from the household's electricity 
consumption data measured by an off-the-shelf smart meter. In this paper, we 
develop a method to determine the sensitivity of a household to outdoor 
temperature and the times of sunset/sunrise, and use those coefficients to 
improve the performance of our household classification system. We further 
investigate the relevance of different features for such a system as well as 
the required granularity of input data. Our evaluation is based on smart meter 
data collected at a 30-minute granularity in more than 4000 Irish households 
over a period of 75 weeks. The results show that - although space heating in 
Ireland is mostly performed using oil or gas - we can improve accuracy by up 
to 2.3 percentage points using temperature and daylight coefficients. The 
characteristics floor area, type of dwelling, and percentage of installed 
energy-efficient light bulbs particularly benefit from temperature and 
daylight coefficients. Finally, we show that semi-hourly or hourly data is 
beneficial over daily meter readings as our analysis performs on average 6.6 
percentage points better.