QUB: A fast dynamic method for in-situ measurement of the whole building heat loss

Alzetto, Florent; Pandraud, Guillaume; Fitton, Richard; Heusler, Ingo; Sinnesbichler, Herbert

doi:10.1016/j.enbuild.2018.06.002

Cited by 36 publications

(39 citation statements)

References 7 publications

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“…• on a house constructed inside an environmentally controlled chamber at University of Salford (Meulemans, Alzetto, Farmer, & Gorse, 2017;Alzetto, Pandraud, Fitton, & Heusler, 2018;Alzetto, Farmer, Fitton, Huges, & Swan, 2018); • on one of the twin houses of Fraunhofer Institute for Building Physics (Alzetto, Pandraud, Fitton, & Heusler, 2018); • on a house of 280 m 2 in southern France (Mangematin, Pandraud, & Roux, 2012); • on an apartment in a high-raise building in Stockholm (Meulemans J. , 2018).…”

Section: /26mentioning

confidence: 99%

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

Ghiaus

Alzetto

2019

Journal of Building Performance Simulation

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Quick U-building (QUB) is a method for short time measurement of energy performance of buildings, typically one night. It uses the indoor air temperature response to power delivered to the indoor air by electric heaters. This paper introduces a method for estimating the expected measurement error as a function of the amplitude and the time duration of the input signal based on the decomposition of the time response of a state-space model into a sum of exponentials by using the eigenvalues of the state matrix. It is shown that the buildings have a group of dominant time constants, which gives an exponential response, and many very short and very large time constants, which have a small influence on the response. The analysis of the eigenvalues demonstrates that the QUB experiment may be done in a rather short time as compared with the largest time constant of the building.

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Section: /26mentioning

confidence: 99%

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

Ghiaus

Alzetto

2019

Journal of Building Performance Simulation

Self Cite

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“…This can happen only if the duration of the two phases, i.e., heating t (1) and cooling t (2) , increase to such extent that all the exponentials decay, except the last one, exp(−t/τ n ). This is considered as sufficient time length for QUB experiment and if this time is shorter than one night, then QUB experiment can be performed during a single night [16].…”

Section: Of 24mentioning

confidence: 99%

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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“…In search for alternative approaches, several researchers have developed on-site measurement methods that are capable of assessing the actual, as-built envelope performance. These dedicated tests include the coheating test [7,8], the Short Term Energy Monitoring (STEM) using Primary and Secondary Term Analysis and Renormalization (PSTAR) technique [9,10], the Quick U-value of Building (QUB) test [11,12], and the In Situ Assessment of the Building EnveLope pErformances (ISABELE) method [13][14][15]. Both the experimental design of these tests and the data analysis methods applied afterwards take the single-zone heat balance (Equation 5) as a starting point.…”

Section: Hlc T = H Tr;t + H In F ;T )mentioning

confidence: 99%

Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

et al. 2019

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Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

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QUB: A fast dynamic method for in-situ measurement of the whole building heat loss

Abstract: QUB is an innovative method enabling the experimental measurement of the total heat loss coefficient (HLC) of a building envelope in one night only. It is based on a simple theory, yet can be demonstrated to be accurate

Cited by 36 publications

References 7 publications

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

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

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

Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

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