Quantifying the impact of occupancy sensor errors on energy savings, thermal comfort, and peak demand in residential smart thermostats

This study investigates how uncertainty in occupancy sensor performance propagates through residential smart-thermostat control and shapes energy savings, thermal comfort, and peak demand. A four-step framework was used. First, stochastic models of occupancy schedules and sensor accuracy were calibrated using long-term field data from a single-family home in Texas. Second, these models were embedded in an EnergyPlus simulation platform via PyEMS to dynamically couple occupant behaviour, sensing errors, and building physics. Third, over ten thousand Monte Carlo simulations were conducted on an HPC cluster across seven IECC climate zones, varying setpoints, occupancy patterns, and sensor sensitivity. Fourth, standardized metrics were used to quantify energy, comfort, and demand impacts. Results show that sensor uncertainty substantially widens the performance range of smart thermostats. High sensor accuracy does not guarantee optimal outcomes, as false-positive and false-negative errors propagate nonlinearly through HVAC operation. Moderate sensitivity levels (0.4–0.7) best balance energy efficiency and comfort.