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

Yong Li, Juan Castiglione, Rodrigo Astroza, Yuxiang Chen

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

5 Citas (Scopus)

Resumen

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.
Idioma originalInglés estadounidense
Número de artículo107184
PublicaciónBuilding and Environment
Volumen188
DOI
EstadoPublicada - 15 ene. 2021

Nota bibliográfica

Funding Information:
The authors would like to acknowledge Dr. A. K. Athienitis and his research team at Concordia University for providing the data. The financial support for this research work is partially provided by Natural Sciences and Engineering Research Council of Canada (NSERC), the University of Alberta, and the Chilean National Commission for Scientific and Technological Research (CONICYT), FONDECYT project No. 11160009).

Funding Information:
The authors would like to acknowledge Dr. A. K. Athienitis and his research team at Concordia University for providing the data. The financial support for this research work is partially provided by Natural Sciences and Engineering Research Council of Canada ( NSERC ), the University of Alberta, and the Chilean National Commission for Scientific and Technological Research ( CONICYT ), FONDECYT project No. 11160009).

Publisher Copyright:
© 2020 Elsevier Ltd

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