Buildings are complex systems with dynamic loading and ever-changing usage. Additionally, there is a need to reduce unnecessary energy consumption while increasing occupant health in buildings via implementation of manual fault detection with available building design programs. However, a common problem with the current lineup of programs is that they require extensive inputs for material properties and usage loads; this results in spending extensive amounts of time performing model calibration and having to adjust multiple values (sometimes hundreds) to bring a model in alignment with actual building use. However, a simplified physics-based model (SPBM) can achieve a level of modeling accuracy sufficient for automatic fault detection with as few as ten automatically calibrated unknown parameters. Obviously, other simplified building energy models exist; however, these often rely on ignoring important details, such as humidity, CO2, and per-hour performance, or implement averaged numerical estimations. Due to the limitations of current modeling programs, some development has begun on rule-based and component-based fault detection by several companies and researchers. While component-based fault detection is effective, it relies on accurate sensor readings and does not account for actual building performance. A suitable rigorous physics-based model has not been developed for the purpose of fault detection. Therefore, by comparing the accuracy of an automatically calibrated SPBM with real-world building performance and high-fidelity building energy models will provide baseline knowledge about if such a model can even achieve a high enough level of fidelity to reliably represent the complexity of a building.

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