Investigation of smart thermostat fault detection and diagnosis potential for air-conditioning systems using a Modelica/EnergyPlus co-simulation approach

Abstract

Smart thermostats offer low-cost alternative for fault detection and diagnosis (FDD) in residential air-conditioning (AC) systems. However, smart thermostats do not directly measure AC performance, but measures indoor air conditions which reflect the indoor air responses to both AC operations and other factors like weather and building gains. Estimating uncontrollable building gain disturbances is essential to differentiate them from AC impacts. Since the uncontrollable disturbances are difficult to monitor using smart thermostat data, there is need to investigate the actual capability of smart thermostats for FDD. In this study, an integrated building and Vapor Compression cycle-based AC model was developed and simulated using EnergyPlus/Spawn and Dymola. The simulation was performed under ten scenarios covering relevant gains, low charge, and low indoor airflow faults. The sensitivity of the time-to-cool feature under these scenarios was studied. The results show about 10% time-to-cool increase from varied internal gains and infiltration. Meanwhile, low charge at 10%, 20% and 30% severity increased time-to-cool by about 3%, 6%, and 20%, respectively, suggesting that if these gains are neglected in an FDD method, it might not be possible to detect low charge below 30% using smart thermostat data since even without a fault, the combined impact of those gains already exceeded that for 20% low charge. For low indoor airflow at 10%, 20% and 30% severity, the increase in time-to-cool was about 3%, 9% and 12%, meaning that only low indoor airflow fault above 30% can be confidently detected if those gains are not captured. Thus, this study demonstrates the need to consider some of these gains in developing smart thermostat based FDD algorithms.