Minimizing User Burden in Building Energy Analysis
- ThermoAnalytics, Inc., Calmut, MI (United States)
Infrared (IR) imagery can provide valuable data to building energy auditors and the developers of Building Energy Models (BEM). IR by itself is not a panacea to the challenges facing energy auditors and modelers, but when combined with other techniques and Open Studio software, IR can play an important role in the minimization of the burden of creating building energy models and the diagnosing of building energy problems. While IR can be used to detect and quantify many aspects of a building’s thermal and energy performance, in Phase I we focused on a single capability: determining the R value of exterior walls. Two key attributes of using IR methods for R value determination is that IR methods measure the actual, installed performance in-situ – as opposed to relying on manufacturer or handbook values – and they can integrate performance over a wide area and thus can accurately account for thermal bridging and spatial anomalies such as “spotty” insulation installation. There is a trade between user burden and accuracy when using IR to determine a wall’s R value. The least burdensome approach is computing R values from a single IR snapshot. The most burdensome method we considered was to combine IR images with thermocouple and heat flux sensor (HFS) measurements made over the course of several days. The more effort users expend in making the measurements, the more accurate and robust the method becomes. Through a series of field tests at the ThermoAnalytics Inc. (TAI) HQ at Calumet, MI and a simulation of a 1980s era office building in Flagstaff, AZ we investigated methods to compute the R value of exterior walls using data from IR cameras, thermocouples and heat flux sensors. To compute R values the heat flux through the wall, along with temperatures on either side of the wall, must be known, modeled, or estimated – and the temperature difference through the wall should be at least 10 C. If only outdoor IR images are used in the method, the inside ambient air temperature must be estimated, and a simulation model created to predict heat fluxes and the indoor wall temperature. The accuracy of the resulting R value depends on the accuracy of the simulation and the assumptions it employs. Applying thermocouple sensors, either as iButtons or as part of a wireless sensor network, to the walls allows for the acquisition of temperatures over time. The longer the duration of the measurements, the more accurate and robust the method will be. Even with this level of measurement effort, modeling is still required to calculate the heat flux through the wall. The addition of a heat flux sensor alleviates the need for any modeling, and that results in improved accuracy. Driving the accuracy of the methods are several factors that can be quantified. How well are the wall surface temperatures known? Modeling the heat flux through walls requires knowing the details of the surrounding radiant environment and the heat transfer due to convection. How well are these boundary conditions known? Even when the heat flux is measured, error can arise when some of the measured heat is not conducted through the wall but rather goes into the thermal mass of the wall. Thermal mass effects occur when conditions and the heat flux values are fluctuating. A technique that is not robust will suffer in accuracy when these fluctuations occur. The R value determination method must be incorporated into the Open Studio process. In our proposed process, IR images and other measured sensor data are input into a module that would send R value estimates to a building library developed by team member Skidmore, Owings, & Merrill (SOM). This library of parameterized building models would use these R values along with information about the building’s age, type, and location to query the Open Studio BCL to identify the most likely constructions for walls, roof, and windows. Being based on the IMF feature of Energyplus, the SOM building library can quickly produce Energyplus models of buildings based on a limited number of user inputs.
- Research Organization:
- ThermoAnalytics, Inc., Calmut, MI (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- Contributing Organization:
- Skidmore, Owings, & Merrill (SOM)
- DOE Contract Number:
- SC0012020
- OSTI ID:
- 1173141
- Report Number(s):
- DOE-TAI-12020
- Resource Relation:
- Related Information: SBIR report that has passed its protection period.
- Country of Publication:
- United States
- Language:
- English
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