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Proceedings of eSim 2016: 9th Conference of IBPSA-Canada

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Building zone fault detection with Kalman filter-based methods

Zixiao Shi, William O’Brien, H.Burak Gunay

Abstract: Fault detection and diagnostics (FDD) is important to maintain proper operation of the building systems. However, most research is focused on heating, ventilation and air conditioning (HVAC) systems FDD, while little work has been done on zone level diagnostics. A building zone is a complex system, but usually equipped with fewer sensors, making the FDD process very challenging. A Bayesian Filter can be used to recursively learn states and parameters of a dynamic system with a limited number of measurements, making it a potential candidate for thermal zone FDD applications. The study in this paper implements a fault detection algorithm for thermal zones using Kalman Filter-based methods with a reduced order energy balance model, and tests its performance using both simulation and experimental data.
Pages: 120 - 131
Paper:
esim2016_37-108