Journal of the Architectural Institute of Korea Structure & Construction (대한건축학회논문집:구조계)

Architectural Institute of Korea (대한건축학회)
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Particle Filter for Estimation of Uncertain Variables in Building Simulation Models

파티클 필터를 이용한 건물 에너지 시뮬레이션 모델의 미지변수 추정

  • Received : 2017.07.05
  • Accepted : 2017.08.28
  • Published : 2017.10.30
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Abstract

Kalman filtering has been used to estimate state variables of a building simulation model such as room air or surface temperatures. However, the Kalman filter can be used only for a linear model and unfortunately most of the building models are strongly non-linear. For example, the convective heat transfer coefficient is strongly influenced by state variables such as room or surface temperatures. In this study, the authors introduce the particle filter (PF), one of the representative nonlinear filters. The particle filter can estimate state variables and uncertain model parameters simultaneously by using the state augmentation technique. For a virtual experiment, a single zone model was developed based on the heat balance method. A series of experiments were conducted to test PF's estimation performance with regard to three uncertain model parameters (the heat capacity of the room air, indoor convective heat transfer coefficient, and the thermal resistance of a wall) and unknown internal heat gain. It is concluded that PF can successfully estimate the aforementioned four parameters. In addition, the more particles are used, the more accurate estimation can be achieved.

Keywords

Acknowledgement

Supported by : 국토교통부

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