Abstract :
[en] Smart thermostats are a key technology for reducing residential energy consumption in smart cities, but their real-world effectiveness depends on the interaction between automation, occupant behavior, and the design of behavioral interventions. This study presents a physics-informed assessment of thermostat strategies across Luxembourg’s singlefamily home stock, using an aggregate thermal model calibrated to eight years of hourly
national heating demand and meteorological data. We simulate five categories of behavioral scenarios: dynamic thermostat adjustments, heat-wasting window-opening behavior, flexible comfort models, occupancy-based automation, and a portfolio of four probabilistic nudges (social comparison, real-time feedback, pre-commitment, and gamification). Results show that occupancy-based automation delivers the largest energy savings at 12.9%, by aligning heating with presence. In contrast, behavioral savings are highly fragile, as a stochastic window-opening behavior significantly erodes the 9.8% savings
from eco-nudges, reducing the net gain to 7.6%. Among nudges, only social comparison
yields significant savings, with a mean reduction of 7.6% (90% confidence interval: 5.3%
to 9.8%), by durably lowering the thermal baseline. Real-time feedback and pre-commit-
ment fail, achieving less than 0.5% savings, because they are misaligned with high-con-
sumption periods. Thermal comfort, the psychological state of satisfaction with the thermal environment drives a large share of residential energy use. These findings demonstrate that effective smart thermostat design must prioritize robust, presence-responsive automation and interventions that reset default comfort norms, offering scalable, policy ready pathways for residential energy reduction in urban energy systems.
