A large fraction of energy consumed in households is due to space heating.
Especially during daytime, the heating is often running constantly, controlled
only by a thermostat – even if the inhabitants are not present. 
Taking advantage of the absence of the inhabitants to save heating energy by 
lowering the temperature thus poses a great opportunity. Since the concrete 
savings of an occupancy-based heating strategy strongly depend on the individual 
occupancy pattern, a fast and inexpensive method to quantify these potential 
savings would be beneficial.

In this paper we present such a practical method which builds upon an approach 
to estimate a household’s occupancy from its historical electricity consumption 
data, as gathered by smart meters. Based on the derived occupancy data, we 
automatically calculate the potential savings. Besides occupancy data,
the underlying model also takes into account publicly available weather data
and relevant building characteristics. Using this approach, households with
high potential for energy savings can be quickly identified and their members
could be more easily convinced to adopt an occupancy-based heating strategy
(either by manually adjusting the thermostat or by investing in automation)
since their monetary benefits can be calculated and the risk of misinvestment is
thus reduced.

To prove the usefulness of our system, we apply it to a large dataset containing 
relevant building and household data such as the size and age of several
thousand households and show that, on average, a household can save over 9%
heating energy when following an occupancy-based heating regime, while certain
groups, such as single-person households, can even save 14% on average.