SUMMARY

uildings are complex structures with dynamic loading and ever-changing usage. Additionally, the need to reduce unnecessary energy consumption in buildings is increasing. As a result, buildings and building energy systems should be designed to conserve energy, and buildings should be monitored and evaluated to ensure that the designs are executed properly and that the buildings are operated correctly. Most building designers now use very adequate energy modeling software to support the design task. However, the 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 or having to adjust multiple values to bring a model in alignment with actual building use. As a consequence, the existing software is complex and awkward for efficient monitoring and evaluation, especially for fault detection and diagnosis. Due to the limitations of current modeling programs, development has begun on rule-based and component-based fault detection by a number of companies. However, a suitable rigorous physics-based model has not been developed for the purpose of fault detection. Consequently, this proposed thesis research will include the design, development, evaluation, and testing of a model-based fault detection program and procedure.
This research proposed herein will result in the development, testing, and refinement a simplified by rigorous and complete physics-based model for buildings and building energy systems that is purposely designed and implemented to support fault detection and similar applications. The usefulness and effectiveness of this simplified physics-based model (SPBM) will be demonstrated by comparison with the obvious currently available alternative, a state of the art purely data driven neural network black-box model. The proposed models, a simplified physics-based energy model and a neural network, will evaluate total building performance using weather and minimal load data that is common to most buildings to determine, identify, and measure the impact of building faults. There is currently no whole-building simplified fault modeling program, as such, evaluation of performance and accuracy of such a system to a state-of-the-art machine learning model will provide substantial insight to current and future fault detection methods.