Space heating causes a large fraction of energy consumed in households and in recent years occupancy-based heating systems have become more and more popular. However, there is still no practical method to estimate the potential energy savings before installing such a system. While substantial work has been done on occupancy detection and heating simulation separate from one another, previous work does not address a combination of both which provides an easily applicable method to estimate this savings potential. In this paper we present such a combination of an occupancy detection algorithm based on smart meter data and a household heating simulation, which besides the derived occupancy only requires publicly available weather data and relevant building characteristics. We apply our method to a dataset containing relevant household data of several thousand households and show that, on average, a household can save over 9% heating energy, while certain groups, such as single-person households, can even save 14% on average. Using our 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).