Sequential state prediction and parameter estimation with constrained dual extended Kalman filter for building zone thermal responses

Abstract This article introduces a parameter estimation and state prediction technique called constrained dual Extended Kalman Filter (EKF) that can be used in model prediction controls (MPC) and fault detection and diagnostics (FDD) in building systems. The proposed method is an improvement over the existing nonlinear filter-based methods such as the joint EKF or Unscented Kalman Filter, which is widely adopted in previous building controls research. Case studies using both simulation and real measurements are conducted to demonstrate the proposed algorithm when used for a building thermal zone. Data from parametric simulations are used to test the thermal model's ability to capture parameter variations. Measured data from five identical offices is collected to test the performance of parameter estimates and state predictions under real-life operation. Overall, the proposed algorithm is 25% faster than a conventional EKF with improved numerical stability. It can also help mitigate numerical instabilities seen in previous EKF research. The reduced thermal model used in this paper is also capable of detecting most of the parametric changes to the thermal zone and providing reliable temperature predictions.