Efficiently managing energy usage to balance supply and demand on the electric grid is crucial, especially with the widespread deployment of distributed variable renewable electricity generation. This paper introduces two duty-cycle control methods for heating systems, adjusting thermostat setpoints to limit and shift electricity demand. The control approaches employ innovative techniques, such as adaptive duty cycling, to prioritize household thermal comfort while reducing peak demand. These control methods can respond to signals from the electric grid, including demand targets and time-of-use tariffs, and were tested physically on an electric furnace and heat pump in a test home during winter conditions in 2021 and 2022. The results are given as average demand reductions and energy use impacts with respect to the average indoor-outdoor temperature difference during the control period. For heat pumps, demand limiting control reduced power by 18.5% and 23.3% for indoor-outdoor temperature differences of 30.F and 40.F. Preheating-based demand shifting achieved reductions of 34.8% and 33.2% for the same temperature differences. Electric furnace tests showed demand reductions of 33.8% and 25.3% for demand limiting, and 56.1% and 45.7% for preheating-based demand shifting. These findings highlight the potential for innovative control methods to enhance grid efficiency and reduce energy consumption.

Fault detection and diagnosis (FDD) using smart thermostat data is a relatively new field of research, but one with immediate practical application to residential indoor climate control. This two-part article proposes a statistics-based method for using aggregated data from many residential HVAC systems to identify outliers. These outlier systems can then be related to operational problems, such as inadequate capacity, malfunctioning control strategies, or other soft faults. The first part of the article presents the analytical methodology. For this study, historical and real-time data from thousands of cloud-based thermostats are used to extract features during the cooling mode of operation. First, the data are parsed to identify time segments that are characteristic periods of pseudo-steady-state operation. Then, a multistage fault detection method compares the features among all systems to find statistical outliers. Each feature is analyzed independently using kernel density estimation (KDE) methods. Additionally, multiple features are analyzed simultaneously using multivariate statistics and the Mahalanobis distance ( $D_M$) to determine outliers. Anomalous systems are sorted heuristically, and information from univariate and multivariate methods is combined to more precisely classify systems exhibiting specific faulty behavior. The second part of this article provides some typical case studies, illustrating how this method can be used to identify aberrant behavior across a large number of monitored systems.

Fault detection and diagnosis (FDD) using smart thermostat data is a relatively new field of research, but one with immediate practical application to residential indoor climate control. In this two-part report, a statistics-based FDD method is proposed to identify anomalous residential air conditioning systems using aggregated thermostat data. The proposed method extracts features that characterize system operational behaviors, and determines outliers by comparing the features between thousands of systems. Those outliers are subsequently classified into several categories of anomalous operational behaviors, such as near-continuous operation, poor control, and so on, which are closely related to soft mechanical faults. While the first part of the report details the methodology, this second part provides five case studies to demonstrate the effectiveness of the proposed method. Although these cases should not be considered a complete representation of all kinds of anomalous behaviors, the examples illustrate typical problems, including performance degradation faults with or without affecting occupant comfort, control problems with slight degradation, and oversized systems. Each case study exemplifies one type of behavior that is common among a group of systems, rather than a single outlier that behaves much differently from all others. The case studies illustrate how the proposed fault detection and diagnosis method is capable of identifying malfunctioning systems from a population of residential air conditioning systems.

The vast majority of fault detection and diagnosis (FDD) methods for air conditioning systems are developed for packaged commercial systems. This paper presents a method for obtaining key operating parameters for air conditioning systems (airflow rate, cooling capacity, system efficiency, and refrigerant mass flow) in a way that is well-suited for the residential sector. The method relies on an air-side capacity estimate and will be compared and contrasted with a more traditional method that relies on a refrigerant-side capacity estimate. These methods are compared in terms of their sensitivity to input parameters, their uncertainty in the outputs, and their sensor requirements. The proposed air-side sensing method requires fewer sensors and has a significant advantage for residential split systems because it requires no outdoor unit sensors. The air-side sensing method is then successfully implemented on a field operating residential system. The experimental results show that the proposed method is sensitive to manually introduced airflow faults.