Automatically estimating the savings potential of occupancy-based heating strategies

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