A vectorial descent stepsize for parameter identification of a coupled parabolic PDE-ODE

Kuate, Raphaël; Nassiopoulos, Alexandre; Bourquin, Frédéric

doi:10.1080/17415977.2014.959007

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Moftakharia et al [38] investigated the estimation of unknown thermal design parameters in building systems using numerical simulation and inverse modeling methods. Kuate et al [39] proposed algorithms to reconstruct parameters of a building energy prediction model coupled with mathematical equations at a reduced scale single zone. Another study compared inverse modeling approaches in predicting building energy performance [20].…”

Section: Concept Of the Inverse Modelsmentioning

confidence: 99%

Integrating physics-based models with sensor data: An inverse modeling approach

Hong

Lee

2019

Building and Environment

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Physics-based building energy models (e.g., EnergyPlus) rely on some unknown input parameters (e.g., zone air infiltration) that are hard to measure, leading to uncertainty in simulation results especially for existing buildings with varying operating conditions. With the increasing deployment of smart thermostats, zone air temperature data are readily available, posing a new opportunity for building energy modeling if such data can be harnessed. This study presents a novel inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the zone air infiltration rate and zone internal thermal mass (e.g., furniture, interior partitions), which are two important model parameters with great variability and difficult to measure. This paper introduces the technical concept and algorithms of the inverse models, their implementation in EnergyPlus, and verification using EnergyPlus simulated building performance data. The inverse modeling approach provides new opportunities for integrating data from massive IoT sensors and devices to enhance the accuracy of simulation results which are used to inform decision making on energy retrofits and efficiency improvements of existing buildings.

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Section: Concept Of the Inverse Modelsmentioning

confidence: 99%

Integrating physics-based models with sensor data: An inverse modeling approach

Hong

Lee

2019

Building and Environment

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“…Here we use the results of a sensitivity analysis of building thermal parameters [14] coupled with the conclusions of the studies [25,19] on the parameters identifiability. We also want to accurately reconstruct the main energy flux of the building thus the unknown parameters have to be related thereof.…”

Section: Choice Of the Unknown Parametersmentioning

confidence: 99%

Dynamic building performance assessment using calibrated simulation

Brouns

Nassiopoulos²,

Bourquin

et al. 2016

Energy and Buildings

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International audienceAccurate building performance assessment is necessary for the design of efficient energy retrofit operations and to foster the development of energy performance contracts. An important barrier however is that simulation tools fail to accurately predict the actual energy consumption. We present a methodology combining thermal sensor output and inverse algorithms to determine the key parameters of a multizone thermal model. The method yields calibrated thermal models that are among the most detailed ones in the literature dealing with building thermal identification. We evaluate the accuracy of the resulting thermal model through the computation of the energy consumption and the reconstruction of the main energy flux. Our method enables one to reduce standard uncertainties in the thermal state and in the quantities of interest by more than 1 order of magnitude

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