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Application of Kalman Filter for Estimating a Process Disturbance in a Building Space

Author

Listed:
  • Deuk-Woo Kim

    (Korea Institute of Civil Engineering and Building Technology, Goyang-si, Gyeonggi 10205, Korea)

  • Cheol-Soo Park

    (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon, Gyeonggi 16419, Korea)

Abstract

This paper addresses an application of the Kalman filter for estimating a time-varying process disturbance in a building space. The process disturbance means a synthetic composite of heat gains and losses caused by internal heat sources e.g., people, lights, equipment), and airflows. It is difficult to measure and quantify the internal heat sources and airflows due to their dynamic nature and time-lag impact on indoor environment. To address this issue, a Kalman filter estimation method was used in this study. The Kalman filtering is well suited for situations when state variables of interest cannot be measured. Based on virtual and real experiments conducted in this study, it was found that the Kalman filter can be used to estimate the time-varying process disturbance in a building space.

Suggested Citation

  • Deuk-Woo Kim & Cheol-Soo Park, 2017. "Application of Kalman Filter for Estimating a Process Disturbance in a Building Space," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1868-:d:115547
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    Citations

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    Cited by:

    1. Lukas Lundström & Jan Akander, 2019. "Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings," Energies, MDPI, vol. 13(1), pages 1-28, December.
    2. Marco Massano & Edoardo Patti & Enrico Macii & Andrea Acquaviva & Lorenzo Bottaccioli, 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings," Energies, MDPI, vol. 13(8), pages 1-17, April.

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