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arXiv · 1711.06386

Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization

Tingting Zeng·Jonathan Brooks·Prabir Barooah
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Abstract

We propose a method that simultaneously identifies a linear time-invariant model of a building's temperature dynamics and a transformed version of the unmeasured disturbance affecting the building. Our method uses l1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the slowly-varying nature of occupancy that determines the disturbance. The proposed method involves solving a convex optimization problem that guarantees the identified black-box model possesses known properties of the plant, especially input-output stability and positive DC gains. These features enable one to use the method as part of a self-learning control system in which the model of the building is updated periodically without requiring human intervention. Results from the application of the method on data from a simulated and real building are provided.

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Tingting Zeng, Jonathan Brooks, Prabir Barooah. 2017-11-17. Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization. https://arxiv.org/abs/1711.06386

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