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1. 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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2. Simultaneous identification of dynamic model and occupant-induced disturbance for commercial buildings

   Coffman, Austin; Barooah, Prabir (January 2018, Building and environment)

   A model of a building’s thermal dynamics is needed for prediction-based control. The task of identifying a thermal dynamic model is made challenging by the presence of large unmeasured disturbances, especially the heat gain due to the occupants. In fact, identification of this “occupant-induced load” is also valuable for predictive control—especially in commercial buildings. We propose a method to identify both a model (of resistance-capacitance network type) and the unmeasured disturbances from measured input-output data. The method is based on the insight that the main contributor to the unmeasured disturbance, the occupant-induced load, is piecewise constant, especially in commercial buildings. This can be used to construct an augmented dynamic model so that disturbance estimation is converted to a state estimation problem. An outer-loop optimization identifies the best-fit parameter values. The effectiveness of the method is evaluated using data from a simulation model (under both open and closed-loop operations) and a real building. 

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3. Suppressing unknown disturbances to dynamical systems using machine learning

   https://doi.org/10.1038/s42005-024-01885-2  

   Restrepo, Juan_G; Byers, Clayton_P; Skardal, Per_Sebastian (December 2024, Communications Physics)

   Abstract Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. Here we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with the identification of both deterministic and stochastic unknown disturbances to an analog electric chaotic circuit and with numerical examples where a chaotic disturbance to various chaotic dynamical systems is identified and suppressed. 

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4. Identification of Network Dynamics and Disturbance for a Multi-zone Building

   Zeng, Tingting; Barooah, Prabir (December 2018, 2nd {IFAC} Conference on Cyber-Physical and Human Systems)

   We propose a method that simultaneously identifies a sparse transfer matrix and disturbance for a multi-zone building’s dynamics from input-output measurements. An l1 -regularized least-squares optimization problem is solved to obtain a sparse solution, so that only dominant interactions among zones are retained in the model. The disturbance is assumed to be piecewise-constant: the assumption aids identification and is motivated by the nature of occupancy that determines the disturbance. Application of our method on data from a simulation model shows promising results. 

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5. Detecting disturbances in network-coupled dynamical systems with machine learning

   https://doi.org/10.1063/5.0169237  

   Skardal, Per Sebastian; Restrepo, Juan G (October 2023, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   Identifying disturbances in network-coupled dynamical systems without knowledge of the disturbances or underlying dynamics is a problem with a wide range of applications. For example, one might want to know which nodes in the network are being disturbed and identify the type of disturbance. Here, we present a model-free method based on machine learning to identify such unknown disturbances based only on prior observations of the system when forced by a known training function. We find that this method is able to identify the locations and properties of many different types of unknown disturbances using a variety of known forcing functions. We illustrate our results with both linear and nonlinear disturbances using food web and neuronal activity models. Finally, we discuss how to scale our method to large networks. 

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