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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. 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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4. 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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5. Monitoring Forest Infestation and Fire Disturbance in the Southern Appalachian Using a Time Series Analysis of Landsat Imagery

   https://doi.org/10.3390/rs12152412  

   Khodaee, Mahsa; Hwang, Taehee; Kim, JiHyun; Norman, Steven P.; Robeson, Scott M.; Song, Conghe (August 2020, Remote Sensing)

   null (Ed.)

   The southern Appalachian forests have been threatened by several large-scale disturbances, such as wildfire and infestation, which alter the forest ecosystem structures and functions. Hemlock Woolly Adelgid (Adelges tsugae Annand, HWA) is a non-native pest that causes widespread foliar damage and eventual mortality, resulting in irreversible tree decline in eastern (Tsuga canadensis) and Carolina (T. caroliniana) hemlocks throughout the eastern United States. It is important to monitor the extent and severity of these disturbances over space and time to better understand their implications in the biogeochemical cycles of forest landscapes. Using all available Landsat images, we investigate and compare the performance of Tasseled Cap Transformation (TCT)-based indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Disturbance Index (DI) in capturing the spectral-temporal trajectory of both abrupt and gradual forest disturbances (e.g., fire and hemlock decline). For each Landsat pixel, the temporal trajectories of these indices were fitted into a time series model, separating the inter-annual disturbance patterns (low frequency) and seasonal phenology (high frequency) signals. We estimated the temporal dynamics of disturbances based on the residuals between the observed and predicted values of the model, investigated the performance of all the indices in capturing the hemlock decline intensity, and further validated the results with the number of individual dead hemlocks identified from high-resolution aerial images. Our results suggested that the overall performance of NDVI, followed by TCT wetness, was most accurate in detecting both the disturbance timing and hemlock decline intensity, explaining over 90% of the variability in the number of dead hemlocks. Despite the overall good performance of TCT wetness in characterizing the disturbance regime, our analysis showed that this index has some limitations in characterizing disturbances due to its recovery patterns following infestation. 

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