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

2017/11/17 by Zeng, Tingting, Brooks, Jonathan, Barooah, Prabir · 1 citation
#FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.1711.06386

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.

Cited by

• Hybrid modeling approach for better identification of building thermal network model and improved prediction

Related

• Adaptive Robust Data-Driven Building Control via Bilevel Reformulation: An Experimental Result
• Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result
• Aggregation and Data Driven Identification of Building Thermal Dynamic Model and Unmeasured Disturbance
• Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings
• Robust Learning Model Predictive Control for Periodically Correlated Building Control