Design of experiments for Quick U-building method for building energy performance measurement

Ghiaus, Christian; Alzetto, Florent

doi:10.1080/19401493.2018.1561753

Cited by 16 publications

(23 citation statements)

References 23 publications

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“…The error remains almost constant after 1-2 h of the QUB experiment, which was in accordance with previously published results [11]. This behavior was explained by the important contribution of the exponentials corresponding to medium time constants (1 … 2 h), which have significant coefficients [5].…”

Section: Time Durationsupporting

confidence: 92%

“…. 2 h), which have significant coefficients [5]. the reference value could be obtained during the first half hour of QUB experiment.…”

Section: Time Durationmentioning

confidence: 85%

“…The derivation of Equation 3from Equation 5is based on the assumptions of homogeneous internal temperature and constant external temperature [12]. Although Equation 5is used in Equation 3to obtain the estimate of the overall heal loss coefficient, it is important to stress that QUB method is not equivalent to fitting the full observed dynamic response of the building to a first order model given by Equation (5). In the QUB method, the fitting is done on a short period at the end of the heating and at the end of the cooling phases.…”

Section: Of 24mentioning

confidence: 99%

“…taking the mean values of H-value estimated over a period longer than three days and multiple of 24 h; -using corrections to compensate for storage effects [5].…”

Section: Introductionmentioning

confidence: 99%

“…The calculated or designed H-value of a building is prone to errors due to simplifications and assumptions used in modeling of the building, difference between the real and the designed structure of the building, change of the thermal properties of the material from the quoted values due to workmanship, moisture transfer and wear and tear [5]. The calculated H-value therefore needs to be validated using different on-site tests, also known as thermal performance tests, which may be categorized as long-term and short-term methods.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Initial and Boundary Conditions on the Accuracy of the QUB Method to Determine the Overall Heat Loss Coefficient of a Building

2020

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The quick U-building (QUB) method is used to measure the overall heat loss coefficient of buildings during one to two nights by applying heating power and by measuring the indoor and the outdoor temperatures. In this paper, the numerical model of a real house, previously validated on experimental data, is used to conduct several numerical QUB experiments. The results show that, to some extent, the accuracy of QUB method depends on the boundary conditions (solar radiation), initial conditions (initial power and temperature distribution in the walls) and on the design of QUB experiment (heating power and duration). QUB method shows robustness to variation in the value of the overall heat loss coefficient for which the experiment was designed and in the variation of optimum power for the QUB experiments. The variations in the QUB method results are smaller on cloudy than on sunny days, the error being reduced from about 10% to about 7%. A correction is proposed for the solar radiation absorbed by the wall that contributes to the evolution of air temperature during the heating phase.

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Section: Time Durationsupporting

confidence: 92%

“…. 2 h), which have significant coefficients [5]. the reference value could be obtained during the first half hour of QUB experiment.…”

Section: Time Durationmentioning

confidence: 85%

Section: Of 24mentioning

confidence: 99%

“…taking the mean values of H-value estimated over a period longer than three days and multiple of 24 h; -using corrections to compensate for storage effects [5].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Initial and Boundary Conditions on the Accuracy of the QUB Method to Determine the Overall Heat Loss Coefficient of a Building

2020

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Influence of natural weather variability on the thermal characterisation of a building envelope

et al. 2021

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The thermal characterisation of a building envelope is usually best performed from on-site measurements with optimised controlled indoor conditions. Conversely, occupant-friendly measurement conditions provide less informative data. Notwithstanding occupancy, the boundary conditions alone contribute to a greater extent to the energy balance, which implies that non-intrusive conditions bring into question the reproducibility and relevance of such measurement. This paper proposes an original numerical methodology to assess the reproducibility and accuracy of the estimation of the overall thermal resistance of an envelope under variable weather conditions. A comprehensive building energy model serves as reference model to produce synthetic data mimicking non-intrusive conditions, each with a different weather dataset. An appropriate model is calibrated from the synthetic data and provides a thermal resistance estimate. The accuracy of the estimates is then assessed in light of the particular weather conditions used for data generation. The originality also lies in the set of weather data that allow for uncertainty and global sensitivity analyses of all estimates with respect to six weather variables. The methodology is applied to a one-storey house reference model, for which thermal resistance is inferred from calibrated RC models. Robust estimations are achieved within 11 days. The outdoor temperature and the wind speed are highly influential because of the large air change rate in the case study.

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Towards the characterization of the heat loss coefficient via on-board monitoring: Physical interpretation of ARX model coefficients

Senave

Reynders²,

Bacher

et al. 2019

Energy and Buildings

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This paper explores the concept of characterizing the as-built Heat Loss Coefficient (HLC) of buildings based on-board monitoring (OBM), via energy consumption and temperature sensors, and time series analysis. It is examined (1) how the coefficients of different Auto-Regressive with eXogenous inputs (ARX) models can be interpreted and (2) whether these conclusions are sensitive to the building envelope assembly or the applied indoor temperature profile. The paper presents a theoretical case study whereby detailed building energy simulations are used to accurately map the impact of physical phenomena on the characterization process. The simulation models and boundary conditions are composed to focus on the link between the estimated ARX-coefficients and the physical driving forces for transmission heat loss to the ground and the exterior environment. The results show how the various ARX model coefficients are linked to specific components of the HLC (e.g. heat transfer through the walls and roof or through the slab-on-ground floor) and to what extent they are affected by the selection of input variables. By monitoring the ground temperature, the transmission heat losses can rather accurately be assigned to either the slab-on-ground or the walls and roof. Without this measurement data, the uncertainty on the estimates increases (ranges of the 95% confidence interval of up to 35% of the mean estimate). Modeling the ground heat losses by a constant intercept term leads to underestimations of the reference HLC of up to 59%, whereas adding heat flux sensors to monitor the transmission heat losses to the ground to the measurement setup allows to assess the transmission heat transfer coefficient to the exterior environment HLC e within 2%.

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Design of experiments for Quick U-building method for building energy performance measurement

Cited by 16 publications

References 23 publications

Influence of Initial and Boundary Conditions on the Accuracy of the QUB Method to Determine the Overall Heat Loss Coefficient of a Building

Influence of Initial and Boundary Conditions on the Accuracy of the QUB Method to Determine the Overall Heat Loss Coefficient of a Building

Influence of natural weather variability on the thermal characterisation of a building envelope

Towards the characterization of the heat loss coefficient via on-board monitoring: Physical interpretation of ARX model coefficients

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