Building envelope performance monitoring is essential to track changes in envelope health and identify critical thermal transmittance and airtightness issues. Inverse model-based envelope characterization methods address the limitations of traditional envelope performance testing methods and allow for continuous monitoring of envelope health using available measurements from the building automation system. However, several challenges are associated with deploying these inverse models, including data collection and processing, practical implementation, and results interpretation. This paper investigates the capability of inverse model-based approaches for monitoring building envelope performance and provides practical guidance for implementing these models. The inverse model-based envelope characterization algorithms are validated using simulated data from a newly constructed academic building in Ottawa, Canada. The practical implementation of the models is demonstrated using data collected from an office building located in Borden, Canada. The data are used to train the inverse models and estimate the buildings' effective thermal transmittance and air permeance. Recommended values are extracted from building codes to quantify deviation from these codes and identify retrofit improvement opportunities. The results show that the inverse model-based methods could estimate thermal transmittance and air permeance with a -5% and 14% error, respectively. A potential reduction of 9% in the office building's heating demand is estimated upon upgrading the thermal transmittance to the current building code recommended values.
| File Size : | 1
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| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 10 |
| Product Code(s) : | D-CH-24-C059 |
| Published : | 2024 |
