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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 Linear Building Dynamic Model and Disturbance Using Sparsity-Promoting Optimization

   Zeng, Tingting; Brooks, Jonathan; Barooah, Prabir (July 2018, 5th International Conference on High Performance Buildings)

   We propose a method that simultaneously identifies a dynamic model of a building’s temperature in the presence of large, unmeasured disturbances, and a transformed version of the unmeasured disturbance. 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 using both open-loop and closed-loop simulation data. Results show that the identified model can accurately identify the transfer functions in both scenarios, even in the presence of large disturbances, and even when the disturbance does not satisfy the piecewise-constant property. 

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3. Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

   Lee, Hyang-Won; Zhang, Jianan; Modiano, Eytan (October 2018, Proc. IEEE Smartgridcomm: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids)

   Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data. 

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4. Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

   Hyang-Won Lee, Jianan Zhang (October 2018, IEEE SmartGridComm)

   Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems.We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data. 

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5. 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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