  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.
