We are well on our way to developing an understanding of what measurable effects common faults in HVAC systems have on power (W) and energy use (kWh) of residential air conditioning units. These faults can include clogging of the outdoor unit from leaves and dust, dirty filters, refrigerant over or under charge, or other issues. By quantifying what effects these faults have, through the use of real time energy use data that a typical HEMS (Home Energy Management System) can record, we hope to develop a low-cost methodology to detect and help diagnose a fault or problem in an HVAC system before it fails or costs the home owner greater energy bills due to inefficiencies in the system.
The first step in this research is to develop models to understand what measurable variables in the energy use data are affected by these faults. This first requires the development of a baseline model that mimics how a correctly-functioning building and HVAC system work. We decided to use two models – one to focus on predicting power (W), and the other on predicting energy use (kWh). Caitlyn has been working on developing these two models.
Building Energy Model (Predicting HVAC Energy Use): The first is a building energy model that mimics the UTest House and its HVAC system at the research campus of UT Austin. Using EnergyPlus (simulation software, see Figure 1) and BEopt (Building Energy Optimization) as the interface, we used the results of the simulation models to analyze the relationship between energy use and outdoor temperature, and peak power draw and outdoor temperature. Figure 2, shows these results, comparing energy use (kWh) to outdoor temperature for each hour of the summer months (May –September) of the simulation.
HVAC Model (Predicting HVAC Power Draw): The second model is the HVAC model that mimics the behavior of the UTest House’s air conditioner. We are using a highly detailed model called ACHP (Air conditioning/ heat pump) (Figure 3). Caitlyn compiled the specs of the air conditioning unit that will be used for testing (Trane 4TTR3030A), and used them as inputs for the model. She has been working on understanding the effects of indoor and outdoor temperature on the power draw (W) of the compressor and condenser fan, and COP (coefficient of performance) (i.e. efficiency) of the system. This provides a baseline, predicted power draw and efficiency of the correctly-functioning HVAC system. Figure 4 shows an example of the variation in power and COP.
By developing these models, we will be able to understand the typical energy usage and power of an HVAC system. Having a “control” is helpful as we introducing faults to the HVAC system and see how these variables change. Caitlyn has also been working on varying flow rates of the condenser at specific outdoor temperatures to understand how a partially blocked condenser fan, one of the common faults being studied, affects power and COP.
The second step is field testing – Melissa has been spearheading this initial effort by identifying the equipment and procedures that related studies have used test the variables we need to measure in this study. She will provide more detailed updates in the next post!
Figure 1: BEOpt Energy Model of the UTest House
The interface BEopt allowed us to easily create a geometric visualization of the UTest House using physical characteristics of the building such as square footage, window areas, and orientation.
Figure 2: Energy Use (kWh) vs. Outdoor Temperature – Building Energy Modeling Results
This graph represents the relationship between outdoor temperature and energy usage. At varying indoor temperature set points, the slopes of the relationship also varies. At an indoor set point of 70°F, the rate of energy usage increases far more quickly with increase in outdoor temperature, than at an 80°F indoor set point.
Figure 3: HVAC Model
The mechanics of an HVAC system shown in this graphic are provided by the ACHP software, and include the compressor, evaporator, condenser, and expansion valve (XV).
Figure 4: Power (W) vs. Outdoor Temperature – HVAC Model Results
This graph shows the effects changes in outdoor temperature have on the predicted power draw (W) and efficiency (COP, %) of the system. With increasing outdoor temperature, power draw increases and efficiency decreases.
SmRTsolutions’ project goal is aimed at increasing the energy efficiency of residential buildings and their systems using Real Time data. We are a group of Architectural Engineering students from the University of Texas at Austin. Our team is made up of, graduate student, Kristen Cetin, undergraduate students Caitlyn Kallus, a sophomore, and Melissa Flores, a freshman. SmRTsolutions’ project goal is aimed at increasing the energy efficiency of residential buildings and their systems using Real Time data.