Parameter-input estimation of RC thermal models of buildings using unscented Kalman filter and nonlinear least square method

Effective building energy management (e.g. temperature control strategies) necessitates reliable and computationally efficient building thermal models. One type of them is the resistor-capacitor (RC) model. However, estimating model parameters and inputs (e.g. solar heat gain) simultaneously is challenging, especially when some of the temperature states are missing due to instrumentation limitations and/or sensor malfunctions. The present study utilizes unscented Kalman filter (UKF) and nonlinear least squares (NLSs) methods for parameters and input estimation of RC models with possible unavailable temperature states. The estimation procedure, mathematical operations and result analysis are presented in detail. To evaluate the capability of the method, two case studies were conducted. The first case study involved a simple, made-up RC model with known parameters, inputs and states, while the second case study used monitored data from a single detached house. The capability of the method was evaluated by comparing the estimated parameters, inputs and states to the corresponding true values in both study cases. The performance evaluation shows that the proposed method can effectively estimate RC model parameters and inputs, even with certain missing states. The proposed method can be employed for timely online updating of RC model parameters to improve response prediction.