Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization
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.
Cite
@article{arxiv.1711.06386,
title = {Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization},
author = {Tingting Zeng and Jonathan Brooks and Prabir Barooah},
journal= {arXiv preprint arXiv:1711.06386},
year = {2020}
}