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

Yong Li, Juan Castiglione, Rodrigo Astroza, Yuxiang Chen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.
Original languageAmerican English
Article number107184
JournalBuilding and Environment
Volume188
DOIs
StatePublished - 15 Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

  • Building thermal dynamics
  • RC models
  • Real-time online prediction
  • State-parameter estimation
  • Unscented kalman filter

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