Least squares parameter estimation of a reduced order thermal model of an experimental building

Dewson, T.; Day, Brian A.; Irving, A.D.

doi:10.1016/0360-1323(93)90046-6

Cited by 51 publications

(18 citation statements)

References 3 publications

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“…6 illustrates variable supply air temperature control strategies used for the ventilated slab system model. [19]. Moreover, Fux et al have shown that a 3R2C thermal network model can accurately predict the room air temperature compared to the measured average room air temperature for a lodging building in the Swiss Alps [21].…”

Section: Fdm Ventilated Slab Modelmentioning

confidence: 99%

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Development of a simulation analysis environment for ventilated slab systems

Park

Krarti

2015

Applied Thermal Engineering

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Section: Fdm Ventilated Slab Modelmentioning

confidence: 99%

“…Eqs. (19) and (20) provide expressions for the absorbed solar radiation for interior surfaces and floor, respectively.…”

Section: Window Thermal Modelmentioning

confidence: 99%

Development of a simulation analysis environment for ventilated slab systems

Park

Krarti

2015

Applied Thermal Engineering

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“…A commonly used grey-box representation of the thermal response of a building due to heat disturbances uses a lumped parameter Resistive-Capacitative (RC) network. This approach for modeling buildings effectively and efficiently represents the energy loss/storage and has been used widely, e.g., in [3,15,8]. Figure 2 shows an example of such a model for a single zone, as used in [3].…”

Section: Model Structurementioning

confidence: 99%

Model-IQ: Uncertainty propagation from sensing to modeling and control in buildings

Behl

Nghiem

Mangharam

2014

2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)

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A fundamental problem in the design of closed-loop CyberPhysical Systems (CPS) is in accurately capturing the dynamics of the underlying physical system. To provide optimal control for such closed-loop systems, model-based controls require accurate physical plant models. It is hard to analytically establish (a) how data quality from sensors affects model accuracy, and consequently, (b) the effect of model accuracy on the operational cost of model-based controllers. We present the Model-IQ toolbox which, given a plant model and real input data, automatically evaluates the effect of this uncertainty propagation from sensor data to model accuracy to controller performance. We apply the Model-IQ uncertainty analysis for model-based controls in buildings to demonstrate the cost-benefit of adding temporary sensors to capture a building model. We show how sensor placement and density bias training data. For the real building considered, a bias of 1% degrades model accuracy by 20%. Model-IQ's automated process lowers the cost of sensor deployment, model training and evaluation of advanced controls for small and medium sized buildings. Such end-to-end analysis of uncertainty propagation has the potential to lower the cost for CPS with closedloop model based control. We demonstrate this with real building data in the Department of Energy's HUB.

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“…is the density of air at the room temperature. V is the volume of the room [2][50]. The heat transfer properties in this case are represented by equivalent electrical components with associated parameters for modeling the thermostatically controlled HVAC system.…”

mentioning

confidence: 99%

Comparison and Simulation of Building Thermal Models for Effective Energy Management

Amara

Agbossou²,

Cardenas³

et al. 2015

SGRE

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Energy consumption reduction efforts in the residential buildings sector represent socio-economical, technological and environmental preoccupations which justify advanced scientific research. These lead to use inverse models to describe thermal behavior and to evaluate the energy consumption of buildings. Their principal goal is to provide supporting evidence of enhanced energy performances and predictions. More specifically, research questions are related to building thermal modeling which is the most appropriate in a smart grid context. In this context, the models are reviewed according to three categories. The first category is based on physical and basic principle modeling (white-box). The second offers a much simpler structure which is the statistical models (black-box). The black-box is used for prediction of energy consumption and heating/ cooling demands. Finally, the third category is a hybrid method (grey-box), which uses both physical and statistical modeling techniques. In this paper, we propose a detailed review and simulation of the main thermal building models. Our comparison and simulation results demonstrate that the grey-box is the most effective model for management of buildings energy consumption.

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Least squares parameter estimation of a reduced order thermal model of an experimental building

Cited by 51 publications

References 3 publications

Development of a simulation analysis environment for ventilated slab systems

Development of a simulation analysis environment for ventilated slab systems

Model-IQ: Uncertainty propagation from sensing to modeling and control in buildings

Comparison and Simulation of Building Thermal Models for Effective Energy Management

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