Data-Driven Identification of a Thermal Network in Multi-Zone Building

Doddi, Harish; Talukdar, Saurav; Deka, Deepjyoti; Salapaka, Murti V.

doi:10.1109/cdc.2018.8619376

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Preliminary work subsumed by this article instantiated to various application domains have appeared in [22] for power grid networks, [26] for thermal dynamics of buildings and [27] for consensus networks. This article is a detailed version with complete proofs with a presentation from a general linear dynamical system perspective and explores connections with physical laws.…”

Section: Introductionmentioning

confidence: 99%

Physics informed topology learning in networks of linear dynamical systems

et al. 2020

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Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that respect flow conservation, the topology of the interactions can be exactly recovered. The class of problems where reconstruction is guaranteed to be exact includes power distribution networks, dynamic thermal networks and consensus networks. The efficacy of the approach is illustrated through simulation and experiments on consensus networks, IEEE power distribution networks and thermal dynamics of buildings.

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Section: Introductionmentioning

confidence: 99%

Physics informed topology learning in networks of linear dynamical systems

et al. 2020

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“…When operational data (measured sensor data) is unavailable and only thermo-physical characteristics are available, the parameters can be determined analytically [121,122], or by developing an analytical model as a reference model using available data and matching the resultant dynamics of the thermal network and reference models [74,80]. However, if there is good availability of measured data but a lack of thermophysical characteristics data, inverse methodologies [123][124][125] are applied to determine parameter values by minimizing the prediction error between the thermal-network model and the measured data [126][127][128][129]. We may categorize parametric identification methods into three groups based on the numerous methods provided in the literature:…”

Section: Parametric Identificationmentioning

confidence: 99%

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

2022

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The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

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“…In literature, we can also find approaches for network topology reconstruction not based on structured identification, such as [11,12,13].…”

Section: Comparison With Literaturementioning

confidence: 99%

Structured identification for network reconstruction of RC-models

Calzavara¹,

Consolini²,

Kavaja³

2020

Preprint

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Resistive-capacitive (RC) networks are used to model various processes in engineering, physics or biology. We consider the problem of recovering the network connection structure from measured input-output data. We address this problem as a structured identification one, that is, we assume to have a state-space model of the system (identified with standard techniques, such as subspace methods) and find a coordinate transformation that puts the identified system in a form that reveals the nodes connection structure. We characterize the solution set, that is, the set of all possible RC-networks that can be associated to the input-output data. We present a possible solution algorithm and show some computational experiments.

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Data-Driven Identification of a Thermal Network in Multi-Zone Building

Cited by 7 publications

References 13 publications

Physics informed topology learning in networks of linear dynamical systems

Physics informed topology learning in networks of linear dynamical systems

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Structured identification for network reconstruction of RC-models

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