Parameter estimation of resistor-capacitor models for building thermal dynamics using the unscented Kalman filter

Yuxiang Chen; Juan Castiglione; Rodrigo Astroza; Yong Li

doi:10.1016/j.jobe.2020.101639

Parameter estimation of resistor-capacitor models for building thermal dynamics using the unscented Kalman filter

Yuxiang Chen, Juan Castiglione, Rodrigo Astroza^*, Yong Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Accurate and computationally efficient building energy models are critical to the development of online or pseudo-online control strategies and other building management activities. However, such models need to overcome the large uncertainty involved with continuously changing occupant activities and building status. The present study uses unscented Kalman filtering (UKF) in the model parameter estimation for simple yet accurate resistor-capacitor (RC) models to develop reliable building energy models. The estimation procedure, mathematical operations, and other estimation enhancing techniques are presented in detail. Synthetic and measured data were used to validate and evaluate the methodology. The obtained model shows better performance when compared with a model that was calibrated using genetic algorithms in a previous study. This remarkable model performance shows that UKF can enable timely online model update and improve the model predictability.

Original language	English
Article number	101639
Journal	Journal of Building Engineering
Volume	34
DOIs	https://doi.org/10.1016/j.jobe.2020.101639
State	Published - 1 Feb 2021

Bibliographical note

Funding Information:
The authors acknowledge the support from the Chilean National Commission for Scientific and Technological Research (CONICYT) , FONDECYT project No. 11160009 . The authors would like to acknowledge Dr. Andreas K. Athienitis and his research team from Concordia University for providing the data. The financial support for this research work is partially provided by the University of Alberta and the Universidad de los Andes .

Funding Information:
The authors acknowledge the support from the Chilean National Commission for Scientific and Technological Research (CONICYT), FONDECYT project No. 11160009. The authors would like to acknowledge Dr. Andreas K. Athienitis and his research team from Concordia University for providing the data. The financial support for this research work is partially provided by the University of Alberta and the Universidad de los Andes.

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

Building energy models
Parameter estimation
RC models
Unscented Kalman filter

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10.1016/j.jobe.2020.101639

Cite this

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abstract = "Accurate and computationally efficient building energy models are critical to the development of online or pseudo-online control strategies and other building management activities. However, such models need to overcome the large uncertainty involved with continuously changing occupant activities and building status. The present study uses unscented Kalman filtering (UKF) in the model parameter estimation for simple yet accurate resistor-capacitor (RC) models to develop reliable building energy models. The estimation procedure, mathematical operations, and other estimation enhancing techniques are presented in detail. Synthetic and measured data were used to validate and evaluate the methodology. The obtained model shows better performance when compared with a model that was calibrated using genetic algorithms in a previous study. This remarkable model performance shows that UKF can enable timely online model update and improve the model predictability.",

keywords = "Building energy models, Parameter estimation, RC models, Unscented Kalman filter",

author = "Yuxiang Chen and Juan Castiglione and Rodrigo Astroza and Yong Li",

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N1 - Funding Information: The authors acknowledge the support from the Chilean National Commission for Scientific and Technological Research (CONICYT) , FONDECYT project No. 11160009 . The authors would like to acknowledge Dr. Andreas K. Athienitis and his research team from Concordia University for providing the data. The financial support for this research work is partially provided by the University of Alberta and the Universidad de los Andes . Funding Information: The authors acknowledge the support from the Chilean National Commission for Scientific and Technological Research (CONICYT), FONDECYT project No. 11160009. The authors would like to acknowledge Dr. Andreas K. Athienitis and his research team from Concordia University for providing the data. The financial support for this research work is partially provided by the University of Alberta and the Universidad de los Andes. Publisher Copyright: © 2020 Elsevier Ltd

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N2 - Accurate and computationally efficient building energy models are critical to the development of online or pseudo-online control strategies and other building management activities. However, such models need to overcome the large uncertainty involved with continuously changing occupant activities and building status. The present study uses unscented Kalman filtering (UKF) in the model parameter estimation for simple yet accurate resistor-capacitor (RC) models to develop reliable building energy models. The estimation procedure, mathematical operations, and other estimation enhancing techniques are presented in detail. Synthetic and measured data were used to validate and evaluate the methodology. The obtained model shows better performance when compared with a model that was calibrated using genetic algorithms in a previous study. This remarkable model performance shows that UKF can enable timely online model update and improve the model predictability.

AB - Accurate and computationally efficient building energy models are critical to the development of online or pseudo-online control strategies and other building management activities. However, such models need to overcome the large uncertainty involved with continuously changing occupant activities and building status. The present study uses unscented Kalman filtering (UKF) in the model parameter estimation for simple yet accurate resistor-capacitor (RC) models to develop reliable building energy models. The estimation procedure, mathematical operations, and other estimation enhancing techniques are presented in detail. Synthetic and measured data were used to validate and evaluate the methodology. The obtained model shows better performance when compared with a model that was calibrated using genetic algorithms in a previous study. This remarkable model performance shows that UKF can enable timely online model update and improve the model predictability.

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Parameter estimation of resistor-capacitor models for building thermal dynamics using the unscented Kalman filter

Abstract

Bibliographical note

Keywords

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