Building model identification during regular operation - empirical results and challenges

Hu, Qie; Oldewurtel, Frauke; Balandat, Maximilian; Vrettos, Evangelos; Zhou, Datong P.; Tomlin, Claire J.

doi:10.1109/acc.2016.7524980

Cited by 16 publications

(25 citation statements)

References 17 publications

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“…For accurate parameter identification, temperatures of neighboring zones should not have strong correlation [24]. Our testbed is a regular office building in operation, thus forced response experiments were performed during Saturdays to (a) increase identifiability of the building model; (b) minimize effects due to occupancy on our data, and thus facilitate subsequent parameter identification; (c) minimize disturbance to building operation [18].…”

Section: B Collection Of Experimental Datamentioning

confidence: 99%

“…In this section, we describe the physics-based modeling approach proposed in [18], which is a Resistance-Capacitance (RC) model obtained using the Building Resistance-Capacitance Modeling (BRCM) MATLAB toolbox [11]. A main advantage of this approach is that the resulting model has a small number of parameters, even for a complex multi-zone building; furthermore, these parameters have strong physical meaning, which aids in their identification.…”

Section: Physics-based Modelmentioning

confidence: 99%

“…In this paper, we identify a data-driven model, using semiparametric regression, and adapt our previous physicsbased model for the same building [18], using a one-year period of experimental data. We provide a quantitative comparison using various metrics, including open-loop prediction accuracy and closed-loop control strategies.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative comparison of data-driven and physics-based models for commercial building HVAC systems

Zhou

Tomlin

2017

2017 American Control Conference (ACC)

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Commercial buildings are responsible for a large fraction of energy consumption in developed countries, and therefore are targets of energy efficiency programs. Motivated by the large inherent thermal inertia of buildings, the power consumption can be flexibly scheduled without compromising occupant comfort. This temporal flexibility offers opportunities for the provision of frequency regulation to support grid stability. To realize energy savings and frequency regulation, it is of prime importance to identify a realistic model for the temperature dynamics of a building. We identify a lowdimensional data-driven model and a high-dimensional physicsbased model for different spatial granularities and temporal seasons based on a case study of an entire floor of Sutardja Dai Hall, an office building on the University of California, Berkeley campus. A comparison of these contrasting models shows that, despite the higher forecasting accuracy of the physics-based model, both models perform almost equally well for energy efficient control. We conclude that the data-driven model is more amenable to controller design due to its low complexity, and could serve as a substitution for highly complex physicsbased models with an insignificant loss of prediction accuracy for many applications. On the other hand, our physics-based approach is more suitable for modeling buildings with finer spatial granularities.

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Section: B Collection Of Experimental Datamentioning

confidence: 99%

Section: Physics-based Modelmentioning

confidence: 99%

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Quantitative comparison of data-driven and physics-based models for commercial building HVAC systems

Zhou

Tomlin

2017

2017 American Control Conference (ACC)

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“…They emphasized that the model must be both accurate and computationally tractable, and many modeling approaches have been advanced in pursuit of that objective [13]- [17]. Sturzenegger et al [18] developed the Building Resistance-Capacitance (RC) Modeling software toolbox for the thermal modeling of buildings, applications of which are reported in [19], [20]. All of the aforementioned works rely more or less on the RC model as the internal thermal model.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Data Center HVAC System with Super-Multipoint Temperature Sensing System

Wasa

Kasajima

Hatanaka

et al. 2018

SICE Journal of Control, Measurement, and System Integration

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: This paper investigates a heating, ventilation, and air-conditioning (HVAC) system in a data center equipped with a previously developed super-multipoint temperature sensing system. This system is expected to be a key technology for reducing the total power consumption of the HVAC system by controlling the inlet temperature distribution of the servers in real time. For this purpose, we present an overview of our fan-control system based on model predictive control. The main objective of this paper is to identify a dynamical model of temperature variations, in order to predict the future evolution of the distribution. However, the spatially high-density temperature data provided by the sensing system is not suited to the needed model accuracy, and the present modeling problem is differentiated from standard ones. We thus present a systematic scheme for the spatial density reduction of sensors by using spectral clustering and graph theory and associated techniques to acquire the dynamical model. Through simulation with real data, we finally show that the developed model achieves an accuracy of 0.58 degrees Celsius on average.

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“…Although these methods are useful, they typically result in very simple models. On the other hand, multizone models have been investigated to identify the thermal dynamics of each zone in multizone buildings [7][8][9]. These models are more accurate and they can be used for advanced control design (e.g., model predictive control [8]).…”

Section: Thermal Modeling Of Buildingsmentioning

confidence: 99%

Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes

Abdel-Aziz

Koutsoukos

2017

Journal of Control Science and Engineering

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Dynamical models are essential for model-based control methodologies which allow smart buildings to operate autonomously in an energy and cost efficient manner. However, buildings have complex thermal dynamics which are affected externally by the environment and internally by thermal loads such as equipment and occupancy. Moreover, the physical parameters of buildings may change over time as the buildings age or due to changes in the buildings' configuration or structure. In this paper, we introduce an online model learning methodology to identify a nonparametric dynamical model for buildings when the thermal load is latent (i.e., the thermal load cannot be measured). The proposed model is based on stochastic hybrid systems, where the discrete state describes the level of the thermal load and the continuous dynamics represented by Gaussian processes describe the thermal dynamics of the air temperature. We demonstrate the evaluation of the proposed model using two-zone and five-zone buildings. The data for both experiments are generated using the EnergyPlus software. Experimental results show that the proposed model estimates the thermal load level correctly and predicts the thermal behavior with good performance.

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Building model identification during regular operation - empirical results and challenges

Cited by 16 publications

References 17 publications

Quantitative comparison of data-driven and physics-based models for commercial building HVAC systems

Quantitative comparison of data-driven and physics-based models for commercial building HVAC systems

Modeling and Identification of Data Center HVAC System with Super-Multipoint Temperature Sensing System

Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes

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