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

   Zeng, Tingting ; Brooks, Jonathan ; Barooah, Prabir ( January 2018 , Automatica)

   We propose a method that simultaneously identifies a dynamic model of a building’s temperature 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 piecewise-constant nature of occupancy that determines the disturbance. We test our method on both simulation data (both open-loop and closed-loop), and data from a real building. Results from simulation data show that the proposed method can accurately identify the transfer functions in open and closed-loop scenarios, even in the presence of large disturbances, and even when the disturbance does not satisfy the piecewise-constant property. Results from real building data show that algorithm produces sensible results. 

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

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