Real-time thermal dynamic analysis of a house using RC models and joint state-parameter estimation

Li, Yong; Castiglione, Juan; Astroza, Rodrigo; Chen, Yuxiang

Abstract

To enable optimal building energy management in response to the ever-changing building and boundary conditions, it is critical to have numerical models that can provide accurate online prediction based on economically measurable inputs and feedback. The present study explores the capabilities of using the unscented Kalman filter (UKF) in combination with resistance-capacitance (RC) models for online estimation of the thermal dynamics of single detached houses. A joint state-parameter UKF estimation approach is applied to estimate unknown state and model parameters by using fictitious process equations to augment the state vector to include model parameters. The performance of this approach is evaluated by comparing the estimated state values to the monitored data. In addition, the prediction capability of the updated model is also investigated. The estimation procedure, mathematical operations, and result analysis are presented in detail. The remarkable model performance achieved shows that the UKF can efficiently improve RC models' predictability and enable timely online model updating and response prediction.

Más información

Título según WOS: Real-time thermal dynamic analysis of a house using RC models and joint state-parameter estimation
Título de la Revista: BUILDING AND ENVIRONMENT
Volumen: 188
Editorial: Elsevier
Fecha de publicación: 2021
DOI:

10.1016/j.buildenv.2020.107184

Notas: ISI