Bryan P. Rasmussen

Actuator hunting is a widespread and often neglected problem in the HVAC field. Hunting is typically characterized by sustained or intermittent oscillations, and can result in decreased efficiency, increased actuator wear, and poor setpoint tracking. Cascaded control loops have been shown to effectively linearize system dynamics and reduce the prevalence of hunting. This paper details the implementation of cascaded control architectures for Air Handling Unit chilled water valves at three university campus buildings. A framework for implementation the control in existing Building Automation software is developed that requires only a single line of additional code. Results gathered for more than a year show that cascaded control not only eliminates hunting in control loops with documented hunting issues, but provides better tracking and more consistent performance during all seasons. A discussion of efficiency losses due to hunting behavior is presented and illustrated with comparative data. Furthermore, an analysis of cost savings from implementing cascaded chilled water valve control is presented. Field tests show 2.2–4.4% energy savings, with additional potential savings from reduced operational costs (i.e., maintenance and controller retuning).

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.

Many fault detection, optimization, and control logic methods rely on sensor feedback that assumes the system is operating at steady-state conditions, despite persistent transient disturbances. While filtering and signal processing techniques can eliminate some transient effects, this paper proposes an equilibrium prediction method for first-order dynamic systems using an exponential regression. This method is particularly valuable for many commercial and industrial energy system, whose dynamics are dominated by first-order thermo-fluid effects. To illustrate the basic advantages of the proposed approach, Monte Carlo simulations are used. This is followed by three distinct experimental case studies to demonstrate the practical efficacy of the proposed method. First, the ability to predict the carbon dioxide level in classrooms allows for energy efficient control of the ventilation system and ensures occupant comfort. Second, predicting the optimal time to end the cool-down of an industrial sintering furnace allows for maximum part throughput and worker safety. Finally, fault detection and diagnosis (FDD) methods for air conditioning systems typically use static system models; however, the transient response of many air conditioning signals may be approximated as first-order, and therefore, the prediction model enables the use of static fault detection methods with transient data (a need that has not been addressed in over 20 years of air conditioning FDD research). In this paper, the equilibrium prediction method's performance will be quantified using both Monte Carlo simulations and case studies.

Control of energy systems in buildings is an area of expanding interest as the importance of energy efficiency, occupant health, and comfort increases. The objective of this study was to demonstrate the effectiveness of a novel predictive steady-state optimal control method in minimizing the economic costs associated with operating a building. Specifically, the cost of utility consumption and the cost of loss productivity due to occupant discomfort were minimized. This optimization was achieved through the use of steady-state predictions and component level economic objective functions. Specific objective functions were developed and linear models were identified from data collected from a building on Texas A&M University’s campus. The building consists of multiple zones and is serviced by a variable air volume, chilled water air handling unit. The proposed control method was then co-simulated with MATLAB and EnergyPlus to capture effects across multiple time-scales. Simulation results show improved comfort performance and decreased economic cost over the currently implemented building control, minimizing productivity loss and utility consumption. The potential for more serious consideration of the economic cost of occupant discomfort in building control design is also discussed.

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.