Review and comparison of current experimental approaches for in-situ measurements of building walls thermal transmittance

Energy & Buildings 203 (2019) 109417

Contents lists available at ScienceDirect

Energy & Buildings journal homepage: www.elsevier.com/locate/enbuild

Review and comparison of current experimental approaches for in-situ measurements of building walls thermal transmittance b ´ Mihaela Teni a,∗, Hrvoje Krstic´ a, Piotr Kosinski a b

Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia Faculty of Geodesy, Geospatial and Civil Engineering, University of Warmia and Mazury in Olsztyn, Heweliusza Street 10, 10-724 Olsztyn, Poland

a r t i c l e

i n f o

Article history: Received 13 May 2019 Revised 21 August 2019 Accepted 4 September 2019 Available online 5 September 2019 Keywords: Building technology In-situ measurements Thermal transmittance (U-value) Building walls

a b s t r a c t Identifying the actual thermal performance of a building envelope is crucial for energy audits when decisions are made concerning energy refurbishment and implementation of energy-saving measures regarding appropriate and cost-effective building technology selection. It has been shown that in-situ measurements are very useful for determining the actual thermal performance of building elements within existing buildings, particularly since values determined on site in real conditions can significantly vary from theoretical values affecting actual building performance, and laboratory methods have limited application in existing buildings. Owing to the observed limitations of the commonly used heat flow metre (HFM) method, new and alternative methods for conducting in-situ measurements of thermal transmittance (Uvalue) have been proposed in the last few years. These efforts have recently resulted in standardisation of the infrared thermography (IRT) method for conducting in-situ measurements of the U-value. This paper presents an overview of the current and most commonly used experimental in-situ approaches for determining the U-value of building walls and provides a comparison between internationally standardised and alternative methods. Each method is evaluated with its advantages and disadvantages, limitations, reported deviations and the measurement procedure employed. A comparison between the most significant existing in-situ methods used for measuring the U-value is presented with the aim of assisting researchers to select an appropriate measurement procedure to employ. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The aim of the energy-efficient building concept is to provide a highly energy-efficient and decarbonised building stock. In this respect, the thermophysical properties of building elements are key factors to consider [1]. However, it can be challenging to identify the thermal properties of building components accurately, and it is particularly challenging in existing historic and heritage buildings owing to their technological complexity and the heterogeneity of material deterioration [2–4]. Numerous studies conducted since the 1990s [5] have shown the existence of a performance gap between measured and simulated energy performances [6–13], as well as a gap between the measured and calculated thermophysical properties of building elements [3,14–21]. The major sources of these performance gaps are mainly ascribed to occupant activities and behaviour, changes made to the materials and design, ageing

∗

Corresponding author. ´ E-mail addresses: [email protected] (M. Teni), [email protected] (H. Krstic), ´ [email protected] (P. Kosinski). https://doi.org/10.1016/j.enbuild.2019.109417 0378-7788/© 2019 Elsevier B.V. All rights reserved.

of the building, construction defects, technological performance, and building operation and maintenance [3,9,22–24]. According to studies, of all the thermophysical properties, the thermal conductivity of building elements has the most effect on the accuracy of energy simulations of heat loss during the design phase [16,25,26]. It has also been shown that even slight changes in the U-value, which is one of the most sensitive parameters used when predicting energy consumption, result in considerable changes in the heating demand [8,12]. Thus, identifying the actual thermal performance of a building envelope is crucial for energy audits when decisions are made concerning energy refurbishment and implementation of energy-saving measures regarding appropriate and costeffective building technology selection. The in-situ U-value measurements have been discussed in a considerable amount of research since the early 1980s [27–29]. Insitu measurements have shown to be very useful for determining the actual thermal performance of building elements in existing buildings, especially since values determined on site in real conditions can significantly vary from theoretical ones. This can affect actual building performance, and laboratory methods are time and cost consuming for application in existing buildings or even

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

2

Nomenclature U q Q or Ø Ti Te



Tsi Tse

βn

Thermal transmittance (U-value) [W/(m2 K)] Density of heat flow rate [W/m2 ] Heat flow rate [W] Interior ambient temperature [K] Exterior ambient temperature [K] Thermal conductance [W/(m2 K)] Interior surface temperature [K] Exterior surface temperature [K] Exponential functions of the time constant

τn

K1 , K2 , Pn , and Qn Dynamic characteristics of the wall which depend on the time constant, τ n hout Outside convection heat transfer coefficient [W/(m2 K)] hin Inside convection heat transfer coefficient [W/(m2 K)] h Total heat transfer coefficient [W/(m2 K)] A Area [m2 ] θn Environmental temperature [°C] θs Surface temperature [°C] θ ni Inside environmental temperature of the region to be measured [°C] θ ne Outside environmental temperature of the region to be measured [°C] Ts Body surface temperature [K] T Surrounding temperature [K] ε Thermal emissivity [-] σ Stephan-Boltzmann constant [W/(m2 K4 )]

impossible to perform, in particular for historically protected buildings. Calculation methods based on EN ISO 6946:2017 have also proven to be unreliable, especially where accurate data on material properties are lacking [19]. According to the Italian standard UNI 10351, differences between laboratory measurements and real thermal conductivity values of new materials range from 5% to 50%, depending on the ageing of the material, type of material, average humidity level, whether the installation was conducted correctly, and thickness tolerance [3,15,16]. Based on numerous studies, it is also found that differences between the U-values of walls measured in situ with different methods and theoretical values range by up to 153% [19]; hence, it is often difficult to decide which measurement procedure should be used. Therefore, to bridge this performance gap, many authors have recently shown an increasing interest in the further development and improvement of existing methods (in addition to studying new methods), to enable on-site assessments of the actual thermal performance of building elements. When developing and improving in-situ measurements it is also important to consider links between the parameters affecting a healthy working environment and U-values. In addition to the thermal envelope quality, its airtightness is one of the main features of which energy consumption depends on [30]. Airtightness is extremely important for meeting not only energy efficiency but also thermal comfort requirements and satisfactory levels of indoor air quality. Adequate levels of airtightness of buildings provide a controlled flow of air through the building envelope, avoiding problems associated with excessive energy consumption, the occurrence and moisture condensation, unhealthy indoor climate, noise, fire resistance, and ultimately problems with the deterioration of the properties of embedded materials and in the worst case deterioration of the structure itself [31,32]. The existence of

the smallest cracks, which are unavoidable when using traditional building techniques, can affect in situ U-value accuracy. Therefore, to ensure the best thermal performance of building components bordering with outside air or unheated space, it is always advisable to combine the application of high-quality thermal insulation of building components with a satisfactory level of airtightness. Limiting airtightness requirements are often to be found in building regulations and the method most often used for measuring airtightness is the pressure difference method (the “Blower Door” Test). The application of the method is described in detail in the ISO 9972:2015 standard [33]. Other parameters affecting the healthy working environment are humidity and thermal bridges. Humidity has a negative effect on the thermal properties of building elements [34] and can cause an entire range of building degradation mechanisms such as bacterial colonization that can result in serious health problems. In general, if the quantity of moisture in building elements is rising, the U-value is rising as well and the faster the building losses or gains heat. Cold surfaces and living in houses that are too cold or too hot can affect occupants’ thermal comfort. The thermal bridge as a localized area with increased thermal conductivity compared to surrounding structures has a similar effect on a healthy living environment and comfort as increased humidity. Except for increased heat loss and reduced effectiveness of insulation, thermal bridges increase the risk of condensation, mold and rot on internal surfaces. Generally, thermal bridges should be avoided whenever it is possible by proper design and construction. According to building regulations influence of thermal bridges needs to be taken into account when calculating annual energy use for space heating and cooling (ISO 520161:2017) and it is necessary to ensure that water vapor will not condense on the inner surface of thermal bridges. When conducting in-situ measurements of U-value areas such as cracks, thermal bridges and high moisture areas should be avoided. To detect those areas, it is recommended to perform a Blower door test and IR thermography. This paper presents an overview of current experimental insitu non-destructive approaches used to determine the U-value of walls. The aim of this paper is to evaluate and compare the existing and most significant standardised in-situ methods with new non-standardised alternative methods, and to extrapolate the most influential factors affecting the accuracy of each measurement procedure. The methods evaluated in this paper are the heat flow metre (HFM) method, infrared thermography (IRT), temperature based method (TBM), natural convection and radiation (NCaR) method, and simple hot box–heat flow metre (SHB–HFM) method. Analysed methods presented in this paper are divided in two groups depending on the measurement set-up: methods with and without use of a heat flow metre, Fig. 1. In the first part of the paper methods based on the direct measurements of the heat flow through the wall using heat flow metre are discussed: the HFM and SHB– HFM method. In the second part of the paper, methods based on a measured or assumed surface heat transfer coefficient without direct measurements of heat flow with heat flow metre are discussed: the IRT, TBM and NCaR method. For presented methods information about the complexity, accuracy, cost, and time involved in executing the current experimental in-situ approaches is given in the third part of the paper; hence, it is useful when selecting an appropriate measurement procedure to employ. It is important to emphasize that presented methodologies are dedicated to homogenous partitions in a distance from thermal bridges. The situation is different in a case of frame partitions filled with material characterised by lower thermal conductivity [35]. In that case, the infrared (IR) inspection should be completed before the measurements to identify areas that have different thermal resistance, thus enabling their measurement. Finally, the U-value should be calculated considering differences in results.

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

3

Fig. 1. Flow-chart of the in-situ methods for U-value assessment.

2. In-situ measurement set-up with use of heat flow metre 2.1. Heat flow metre method HFM is a common non-destructive and standardised method used for estimating the thermal transmission properties of plane building components. Its use is based on establishing a minimum temperature gradient between indoor and outdoor environment temperatures, which ensures that a sufficient heat flow is present. This method is suitable primarily for plane building components with opaque layers that are perpendicular to the heat flow and have no significant lateral heat flow [14]. However, it can also be applied to components consisting of quasi homogeneous layers that are perpendicular to the heat flow and where the dimensions of inhomogeneities close to the HFM are much smaller than its lateral dimension [14]. The first ISO 9869 standard providing guidelines for HFM in-situ measurements of U-value and thermal resistance was introduced in 1994. The standard was subsequently technically revised by ISO 9869-1:2014 in 2014 [14], and according to this standard, the Uvalue measurement procedure is based on direct measurement of the heat flow rate and temperatures on both sides of the element under steady-state conditions. Therefore, it is necessary to place at least one heat flow metre at the surface of the element adjacent to the more stable temperature, and to employ a data logger and two ambient temperature sensors (Te and Ti ) to obtain the U-value of the element being tested, as shown in Fig. 2. In practice, however, steady-state conditions are never encountered, which is why the standard suggests the use of either a hot and a cold box method

(which is usually employed under laboratory conditions), or one of the proposed statistical methods (average or dynamic), which are discussed in the following paragraphs. The average method assumes that the steady state value of the U-value of the building component can be obtained using the averages of values observed over a sufficiently long time period. When using the average method, an estimate of the U-value can be obtained using the following equation [14]:

U

 W  m2 K

n

j=1

= n j=1

,

(1)

where qj [W/m2 ] is the heat flux passing through the unit area of the sample, Tij [K] and Tej [K] are the interior ambient temperature and exterior ambient temperature, respectively, and index j enumerates the individual measurements. After a sufficiently long time period the U-value tends to an asymptote that represents its actual value. The following is valid only if the heat content of the element is the same at the end and the beginning of the measurement and if solar radiation has a negligible influence on the HFM and the temperature sensors. For building elements with high thermal inertia, the average method also proposes an alternative methodology to enable data correction, as it considers the effects of thermal storage. However, results obtained using the average method may be quite inaccurate, unless the storage effect is negligible. Although the average method provides a simple approach, it can necessitate long periods of measurement owing to associated unsteady conditions. However, the dynamic method, which is more sophisticated, and complex compared to the average method, considers temperature and heat flow rate fluctuations by using the heat equation and several parameters. This method was first proposed by Aittomäki and co-workers [36]. According to the dynamic method prescribed in ISO 9869-1:2014, the heat flow rate at time ti is a function of the temperature at that time and at all preceding times, and is defined as follows [14]:

qi

W  m2

= (Tsi,i − Tse,i ) + K1 T˙si,i − K2 T˙se,i +



Pn

n

+

 n

Fig. 2. Schematic diagram of HFM method according to ISO 9869–1:2014 standard.

qj

Ti j − Te j

i−1 

Tsi, j (1 − βn )βn (i − j )

j=i−p

Qn

i−1 

Tse, j (1 − βn )βn (i − j ),

(2)

j=i−p

where  [W/(m2 K)] is thermal conductance, Tsi,i [K] and Tse,i [K] are the interior and exterior surface temperatures taken at the

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

4

times ti, respectively; T˙si,i and T˙se,i are the time derivatives of the interior and exterior surface temperatures, respectively; the variables β n , are exponential functions of the time constant τ n , while K1 , K2 , Pn , and Qn are dynamic characteristics of the wall which depend on the time constant, τ n . To estimate the U-value using the dynamic method, the following procedure is necessary: measure the density of the heat flow together with the interior and exterior ambient temperatures at several time intervals; compute the derivative of the interior and exterior ambient temperatures; take one to three time constants and compute the exponential functions of each time constant, which (under assumption that m time constants are chosen) results in 2 m + 3 unknown parameters forming the heat flow matrix; estimate the heat flow vectors and evaluate the quality of the results by calculating the total square deviation between the estimated and measured values (while considering that the optimum time constant set is the one that provides the smallest square deviation). Therefore, owing to its complexity, the dynamic method is not often used in practice, even though research has shown that differences between the theoretical and measured U-values are smaller when the dynamic method is employed [20], and the resulting values are more accurate and have lower variability when testing conditions are not optimal [20,37,38]. ISO 9869-1:2014 [14] proposes that measurements are conducted for at least three days to estimate the U-value of the element, however only if the temperature around the HFM is stable. If it is not stable, the time interval needs to be extended and measurements may need to be conducted over a period of more than seven days. In addition, during the measurement time, data should be recorded continuously at fixed intervals. Furthermore, the maximum time period implemented between obtaining two records and the minimum test duration should be determined depending on the nature of the element, indoor and outdoor temperatures, and methods used to conduct data analysis. To obtain representative measurements, the direct influence of heating and cooling devices as well as dryer, ventilators, and many other types of equipment which cause forced convection, rain, snow, and solar radiation should be avoided. The location of the heat flux sensor should be investigated by IRT to avoid placement in the vicinity of thermal bridges, cracks, construction joints, and places that are similar sources of errors. Finally, when conducting measurements, the temperature difference should be greater than 10 °C and should remain as constant as possible [39]. Based on research conducted to date, it is apparent that the accuracy of HFM measurements can be affected by many factors. However, it should be pointed out that following factors are not only specific to HFM method and the main are -

shape, size, location, and pasting angle of the HFM [39–42], equipment accuracy [43], orientation of measured wall [44], weather and climatic conditions (wind velocity, solar radiation, and precipitation) [18,20,38,44,45], thermal inertia [42], temperature difference and heat flow [14,39,46,47], direction of heat flow [37,48], moisture content [41,42,46,49,50], temperature fluctuations [41,42,49], test duration [38], faults in the building envelope and age of material [51], data post-processing and analytical skills [20,38,41,52,53].

These factors can generally be grouped into one of three categories: site, building, and operating conditions [54]. According to Albatici and Tonelli [54], factors associated with site conditions include the weather conditions prior to and during the test, such as

wind speed, solar radiation, precipitation, and the typical climate at the site (particularly humidity), which can significantly alter the thermal performance of the building materials. With respect to building conditions, the authors highlighted the major influences as ageing of building materials and proper installation of elements during construction. Finally, for operating conditions, management of building users and maintenance work are highlighted as the most influential factors. Generally, the level of agreement between measured and calculated U-values varies widely, and the degree of discrepancy depends very much upon the type of construction examined [55]. According to the ISO 9869-1:2014 standard, the uncertainty of in-situ measurements obtained by the HFM method ranges from 14% to 28% [14]. To evaluate the accuracy of the HFM method measurement procedure, a summary overview of previous research conducted on different types of walls and wall materials between 20 0 0 and 2017 is provided in Table 1. Table 1 provides the author names and year of study, gives information about methods used (data analysis methods used for processing measurement results obtained by HFM method and analytical methods authors used for comparison), type of wall/wall material and year of building construction, test duration according to authors of the papers, number of walls tested (n), discrepancies between measured and calculated U-values, and finally, the time of year when measurements were conducted. The presented deviation between the results obtained with HFM method and theoretical value is calculated as absolute value using the following expression:

   UHF M − UC   ∗ 100,

UHF M − UC = 

UC

(3)

Based on the previous research overview presented in Table 1, it can generally be concluded that in the case of traditional (historical) building elements, use of the tabulated design method and analytical calculations tend to underestimate the thermal performance of the wall compared to HFM measurement results [3,4,57,58], i.e. measured U-values are lower than calculated one. However, for newer constructions, measured U-values are typically higher than those predicted by calculations, which result in overestimating the actual building energy performance [18,20,24]. Typically, measured values are around 20% higher [55] and in a some cases the difference is up to 100%, especially if a layer of insulating material is present [16]. Evaluation of the most influencing factors affecting the accuracy of HFM U-value measurements still remains an open issue. For example, the latest study of Gaspar et al. [38] focused on the influence of test duration on measurement results, and aimed to evaluate and determine the minimum test duration required to measure the U-value of existing buildings’ façades. Authors compared minimum test duration results determined according to data quality criteria, variability of results criteria, and standardised criteria for different ranges of theoretical U-values and for the same range of average temperature difference. The results showed that the minimum duration of experimental campaigns depends on the façade’s theoretical U-value, stability of climatic conditions, and the calculation method employed; more accurate results were obtained when the dynamic method was used. Given the increased use of new energy efficient building materials resulting in façades with very low U-values, Gaspar et al. [43] also conducted in-situ measurements of facades with low U-values and refined the appropriate conditions required to conduct measurements, with the aim of evaluating and enhancing measurement accuracy. The results showed that temperature sensors had a greater impact on the accuracy of measurements and revealed that the temperature differences must be greater than those indicated in the existing literature. In addition, Evangelisti et al. [21] studied the influence of power-on

Table 1 Summarised review of HFM method studies on U-value assessment of building walls.

Methods

Doran 2000 [55]

- HFM method - correction for thermal storage effects - Calculation procedure described in BS EN ISO 6946 - HFM method – average method - Calculation methods set out in British Standard BS EN ISO 6946:1997 and the BRE publication, Convention for U-value calculations - HFM method – average method - Standard calculation - calculating software, BuildDesk v3.4. based on the standard BR 443 Conventions for U-value Calculations

Baker 2011 [47]

Rye and Scott 2012 [56]

Rhee-Duverne and Paul Baker 2013 [57]

Asdrubali et al. 2014 [18]

- HFM method - Calculation using four thermal conductivities obtained from industry standard design values (BS EN ISO 6946:1997 and the BR443 Convention for U-value calculations) - HFM method - average method - Calculation method - ISO 6946

Evangelisti et al. 2015[19]

- HFM method - average method - Calculation method - UNI EN ISO 6946

Ficco et al. 2015 [46]

- HFM method - average method (U3) - Nominal design data and technical data available in Italian standards (U1) - Endoscopic analysis and core samplings (U2)

Walker and Pavía 2015 [49]

- HFM method - average method - Laboratory-measured and provider values

Gaspar et al. 2016 [20]

- HFM method - average (UHFMa ) and dynamic method (UHFMd ) - Calculation method - ISO 6946:2007

Type of wall/wall materialYear of constructionTest duration

n

Deviation [%]

Test period

- Cavity and timber frame wall - Prior to 1998 - At least 14 days

21

1%–61% Average 21%

Dec 1998–Mar 1999; Nov 1999, Mar 2000

- Cavity, Solid stone and brick - Prior to 1919 - At least 14 days

57

0%–62% Average 24%

Nov 2007–May 2010 (Three heating season)

- Solid cob and stone, timber framed structures with a variety of different infill materials and walls with air gaps - Pre 1919 - At least 14 days - Traditional UK solid brick walls - 18th - 19th century - 3 to 4 weeks

77

1%–126% Average 28%

Winter

18

0%–62% Average 24%

Feb–Apr 2010

6

4%–75% Average 30%

Heating season between 2010 and 2013

3

14%–113% Average 58%

Dec 2014–Jan 2015

6

U3–U1Winter 1%–70%(163%∗ ) Average 24% U3-U2Winter 2%–55%(211%∗ ) Average 13% U3–U1Summer 45%−142% Average 90% U3–U2Summer 62%−264% Average 152% 16%–33% Average 20%

Winter and Summer

- Walls designed using bio-architecture principles and made of thermal blocks - Between 2007 and 2008 - At least 7 days - Tuff blocks, hollow bricks, hollow bricks and concrete - Late 1800s, early 1950s, and 2000s - 8, 12 and 7 days -Tuff with and without polystyrene insulating layer, prebuilt with polystyrene insulating layer, double hollow bricks with insulating material, concrete brick, hollow bricks and lightened concrete blocks wall - 1965, 1970, 1994, 2000, 2010, and 2015 - Longer than 72 h

- Thermal paint, aerogel, cork lime, hemp lime, calcium silicate board, timber fibre board and PIR board insulation on historic solid brick wall - 1805, insulated between Dec 2013 and Apr 2014 - n.a. - Double skin façade with non-ventilated air cavities and internal insulation, single-skin façade without air cavity or insulation - 1992, 1960, 2007 - 72 h

8

3

2%–20% Average 9% UHFMa - Uc 1%–10% Average 3% UHFMd - Uc

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

Author and Year

Sept 2014–Apr 2015

Dec–Apr

(continued on next page)

5

6

Table 1 (continued) Methods

Type of wall/wall materialYear of constructionTest duration

n

Deviation [%]

Test period

Bros Williamson et al. 2016 [24]

- HFM method - Design calculations

2

10%–65% Average 27%

Winter 2012/13 and 2014/15

Lucchi 2016 [3]

-

- Passive house wall with injected polyurethane insulation and timber frame panels with insulation - 2012 - Between 14 to 21 days - Brick masonries - 13th - 20th century - 7 days for walls thickness <0.9 m and 14 days for walls thickness 0.9–1.1 m

7.7%–46.5% Ut –UHFM 1.6%–46.5% Ua –UHFM ∗∗ 4.6%–18% Uc –UHFM ∗∗ 38%–47% Ut –UHFM ∗∗ 3%–54% Uc –UHFM ∗∗ 8%–54% Average 25%

Two winter seasons

Lucchi 2017 [4]

Hoffmann and Geissler 2017 [45]

∗ ∗∗

HFM method (UHFM ) - mainly average method Tabulated design method (Ut ) Abacus of masonry structures (Ua ) Analytical calculation (Uc )

- HFM method (UHFM ) - mainly average method - Standard suggestions - UNI 10,351 and UNI 1745 (Ut ) - Calculation method - ISO 6946 (Uc ) - HFM method - Calculation method - ISO 6946:2007

- Stone masonries - 12th - 18th century - 7 days for walls thickness <0.9 m and 14 days for walls thickness 0.9–1.1 m - Uninsulated basement walls - ∼1600, 1850, 1905, 1925, 1953, 1962/63, and 1965 - At least 72 h

Highest deviation of 163% and 211% was obtained for light component and HFM that used indirect method. Average cannot be determined, the deviations given in the table correspond to deviations declared by the author.

14

∗∗

10

9

Two winter seasons

Jan–Feb 2016

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

Author and Year

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

and -off of a heating system when evaluating in-situ U-values. The authors proposed a new post-processing method for data analysis called the ‘linear trendline’ approach. The proposed approach consists of generating a linear trendline related to indoor air temperature values while the data is processed in order to keep out values which are higher than the line. Starting from simulations and on-site measurements authors obtained satisfying preliminary results by reducing the difference between the measured and the calculated U-values. Currently, there are some attempts to propose different dynamic analysis approaches for the U-value assessment based on the HFM approach. Starting from first dynamic methods [36,59] a lot of progress has been made in recent years by using advanced dynamic data analysis methods in the area of increasing accuracy and reducing the necessary measurement time under different seasonal boundary conditions. Anderlind [60] suggested multiple regression method to eliminate the influence of varying outdoor temperature which worked extremely well, both in winter climate and in summer climate and reduced the measuring time. Further progress in dynamic data analysis field of study led to the use of black-box dynamic models based on a statistical autoregressive approach [61–64] and different stochastic grey-box models [65– 68] for building components physical parameters identification. All of these methods have different levels of complexity and accuracy, and need prior system modelling and system identification to obtain the required parameters [69]. To date, in few studies several available methods have been considered together in order to perform comparative analyses between obtained results or the performances have been tested against different climatic conditions and different measurement time spans [52,67,70]. A large-scale comparison of all techniques’ performances in function of the measurement time span and climatic conditions is given by Deconinck and Roels [71]. The results of the study showed that semistationary and the dynamic analysis methods perform equally well when winter data sets are considered while for spring and summer data sets, only the dynamic methods lead to reliable estimation results. Over short periods of time, Anderlind’s approach had a slightly faster convergence to accurate estimates compared to ARX-models and grey-box models. However, for longer data periods ARX-models and grey-box models were more accurate. Finally, according to Deconinck and Roels study [71] stochastic grey-box modelling is more labour intensive than Anderlind’s regression and ARX-modelling techniques but enables the use of a set of validation tools not included in the other methods. Even though when using dynamic data analysis methods analysis itself is more time consuming and complex, the dynamic methods show to be less sensible to the measurement period, provide more accurate results and shorter the necessary measurement time spans [20,37,71]. Although many extensive studies have been conducted to date, the HFM method has obvious limitations with respect to the type of building studied and the test season during which it is employed [72]. As Rasooli et al. [73] also stated in their research, fluctuations of temperature and heat fluxes on both sides of the building walls continue to be an issue, as is the time delay in the thermal response of walls with a higher thermal mass (because steadystate conditions are never achieved).

dient between indoor and outdoor environment have been proposed. To solve this problem, some authors proposed new methods mainly based on HFM and guarded hot box approach. Combining the advantages of the guarded hot box method and HFM principles, researchers from China proposed the temperature control box-heat flow metre (TCB-HFM) method [77–80], which provides a relatively stable thermal environment as the control temperature box is installed on the inner surface of the element that is being tested. According to the season, the control temperature box can either heat or cool the element that is being tested, thereby creating a temperature difference between indoor and outdoor environment. However, the main limitation of this method is that when conducting summer measurements, the temperature control box needs an auxiliary refrigeration system to provide cooling, which makes it complex and not convenient enough for in-situ measurements [81]. In literature, in-situ measurement of the wall thermal transmittance and some numerical simulation by the TCB-HFM have been performed. In the first of such studies, high measurement accuracies were obtained when compared to design values. Tian [77] measured the thermal transmittance of a sintered clay brick wall in a non-heating period and obtained a difference between measurement and design values of only 0.7%. However, in a study by Zhu et al. [80] conducted on a concrete masonry wall, results showed a deviation of +55% between the design and measured thermal transmittance value; and this result was attributed to high moisture. Nevertheless, this method is still in its exploration stage and associated research is currently lacking to prove its applicability and accuracy. Another method called the simple hot box–heat flow metre (SHB-HFM) method [72] was also proposed by researches from China. The proposed method combines HFM principles and the advantages of the TCB–HFM method. The basic premise of the researchers’ design was to propose an easier and more accurate method for dealing with very complex in-situ conditions and China’s continuous urbanisation situation, where many new buildings and vast constructions need to be measured. The method uses simple experimental equipment, avoids the seasonal restriction of the HFM method and heavy equipment of the hot box method, hence creating a relatively stable thermal environment by using a simple hot box. A simple hot box, electrical heating element, HFMs, T-thermocouples, and data logger are the main instruments required for the U-value assessment, Fig. 3. The main difference between this method and the TCB-HFM method is that the SHB-HFM method is based on the original temperature difference between the inside and outside environments; hence, it can operate regardless of the season, and the heating operation enables creation of

2.2. Simple hot box–heat flow metre method In-situ U-value measurements must be conducted under a minimum temperature difference between the indoor and outdoor environments to ensure that there is a measurable heat exchange across the building envelope, and according to literature, the test must be conducted under a temperature difference greater than 10 °C [4,39,74–76]. Therefore, several approaches dealing with the easier establishment and control of the minimum temperature gra-

7

Fig. 3. Schematic diagram of SHB-HFM method in summer conditions.

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

8

the wall temperature difference in all circumstances. The simple hot box is simply a temperature control device that is installed on the higher temperature side (i.e. on the internal surface in winter and on the external surface in summer, as shown in Fig. 3). In addition, as this method only requires heating equipment, both the cost of the measurement system and the difficulty in obtaining measurements are reduced compared to the TCB-HFM method. The main principle of the method is that the hot box is placed on the surface of a wall. It is advised that the wall is heated inside the box area to a wall temperature of 20 °C [81], to establish a temperature difference. Depending on the box size, a onedimensional (1-D) heat transfer zone is then formed in the central area of the wall. By placing thermocouples and HFMs on the 1-D heat transfer area on both inner and outer surfaces, a highly accurate measurement of wall thermal transmittance can be obtained [72]. The method uses the same data processing method as the HFM method, and the wall U-value is obtained using the following equation [72]:

U=

1 + hout

N

n=1

(TI,si,n − TI,se,n )

N

n=1

qi,n

+

1 hin

−1 ,

(4)

where, hout [W/(m2 K)] and hin [W/(m2 K)] are the outside and inside convection heat transfer coefficients, respectively; Tl,si,n [°C] and Tl,se,n [°C] are the nth measurement values of temperature on the inner and outer surfaces, respectively; qi,n [W/m2 ] is the nth measurement value of the heat flow on the inner surface, and N is the sum of the measurement numbers during one data processing cycle, which is an integral multiple of 24 h. The first study regarding the SHB-HFM method conducted by Meng et al. [72] verified the feasibility of using the SHB-HFM by conducting in-situ measurements of wall U-value. The authors conducted U-value measurements of one brick wall of a new building, explored the influence of various factors on test accuracy (such as the location deviation of test sensors), established and verified the mathematical model, and investigated the influence of box size on measurement accuracy. The research results showed that the SHBHFM method has an adequate test accuracy compared to design values; however, the test errors owing to location deviation of the sensor increase from −5.97% to −8.14% and −9.60%. In addition, measured U-values were always lower than the design values. To improve measurement accuracy further, the authors proposed the use of two HFMs arranged on the inner and outer surface, respectfully, and presented the average value obtained as the test result. Regarding mathematical simulation, the simulation and measurement values showed a good consistency under the same measurement conditions. Finally, considering box dimensions, the authors concluded that box dimension has a significant influence on test accuracy given that a moderate enlargement of the box dimension is an efficient way to improve in-situ test accuracy. In subsequent research, Meng et al. [81] determined the minimum and optimum box dimensions when considering three influential factors (wall thickness, wall equivalent thermal conductivity, and temperature difference), with the aim of assisting researchers in performing in-situ measurements; results showed that the minimum box dimension should increase linearly with the increasing thickness of the wall or equivalent thermal conductivity. With respect to the influence of the temperature difference on minimum box dimensions, results showed that when the temperature difference is higher than 20 °C, the minimum box dimensions are close to constant with an increase in the temperature difference. Through use of the multiple linear regression method, the authors defined the quantitative relation between the minimum box dimension and wall thickness, wall equivalent thermal conductivity, and temperature difference. Based on the minimum box dimension that en-

sures formation of the 1-D heat transfer zone, the authors also defined the optimum box dimension, which also considers the additional area occupied by both HFMs and thermocouples. Another research found in literature used a similar method for the U-value measurement based on the HFM and hot box approach. The research was performed by Scarpa et al. [82] using a two hot boxes placed on the internal side of the building construction. Table 2 shows the summarised results of U-value measurements obtained using the SHB-HFM method. The presented deviation between the results obtained with SHB–HFM method and theoretical value is calculated using the following expression:

   USHB−HF M − UC   ∗ 100, U

USH B−H F M − UC = 

(5)

C

As the SHB–HFM method is relatively new, it has not been the subject of many studies. It is thus important to state that measurements have only been recorded during summer using only one type of wall. Even though high measurement accuracies have been obtained in these initial studies compared to design values, which makes this method promising, research is lacking, and the method requires further testing to evaluate its reliability and applicability. 3. In-situ measurement set-up without use of heat flow metre 3.1. Infrared thermography method In addition to the HFM method, the IRT method is one of the most widely employed methods used in thermal building diagnosis. IRT applications in thermal diagnostics began to be widely commercialised in the early 1990s [83]. In building evaluations, IRT has primarily been used to qualitatively evaluate buildings and detect defects [75], such as places where air leakage typically occurs [31,84–86], cracks [87], insulation continuity [84,88], thermal bridges [84,89,90], plaster detachment [91], moisture and condensation [85,87,92–94], delamination [87,95], defective services [88] and as a support when conducting HFM measurements with respect to the location of the heat flux sensor [14,49,96]. However, its range of application today is extensive, owing to its noninvasiveness and the possibility of rapid in-situ inspection of relatively large areas in real time and the latest studies are dealing with improvement of quantitative assessment of thermal bridges by using different methodologies and mathematical tools [97–101]. Until quite recently, U-value assessment of a building envelope by means of quantitative IRT has been a topic of numerous studies without any prescribed standard [74,102,103]. However, in August 2018, as a result of previously conducted comprehensive studies, an international standard was introduced that describes the use of the IRT method for measuring the thermal resistance and U-value of opaque building elements in existing buildings, while conducting observations of highly emissive and diffuse surfaces using an IR camera [76]. In this respect, ISO 9869-2:2018 defines the apparatus used, calculation procedures, measurement method, and associated uncertainties. The method is prescribed for building components that have a relatively small heat capacity per unit area of approximately 30 kJ/(m2 K) or less. To measure the U-value of walls, ISO 9869–2:2018 requires an IR camera (with a minimum overall blackbody temperature range of −20 °C to 100 °C, and a minimum of 80 mK on a 30° blackbody object temperature thermal sensitivity), a heat transfer coefficient sensor (h), environmental temperature sensors both inside and outside the building (θ ni and θ ne , respectively), thermocouple thermometer, and a data logger, as shown in Fig. 4. The basic principle of the IRT method is that it measures the amount of irradiance of regions in contact with the outside air using the surface temperature, total heat transfer coefficient, and en-

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

9

Table 2 Summarised review of study based on SHB-HFM method for U-value assessment and associated results. Author and year

Methods

Type of wall/wall material

n

Deviation [%]

Test period

1

4%–7%

7–15 Aug 2013

Year of construction Meng et al. 2015 [72]

- SHB–HFM method - Calculation method

- Self-insulation brick wall - n.a.

the building’s doors, and openings (such as windows) should be blocked using curtains and blinds. For light walls, the standard proposes that measurements are conducted on at least three consecutive nights from one hour after sunset to sunrise in synchronically recorded intervals of 30 min or less; while for heavy walls, the measurement time employed should be longer. Measurements should be conducted until the results of consecutive three-day measurements fall within a range of ±10%. In cases where conducting consecutive measurements is impossible, a minimum measurement period of one day is acceptable. In the case of a dwelling with a frame structure and a heat capacity of approximately 30 kJ/(m2 K) or less, the measurement period should be three to six hours between 0 0:0 0 and 06:00. According to the standard, the main limitations to obtaining accurate measurements are as follows [76] Fig. 4. Schematic diagram of IRT method according to ISO 9869-2:2018 standard.

vironmental temperature. The amount of irradiance of regions in contact with the outside air when being heated is derived in relation to the inside temperature. The heat flow rate through a part of a building element is evaluated by multiplying the total heat transfer coefficient by the difference between the indoor building element surface temperature and the indoor environment temperature, using the following equation [76]:

Q = h (θn − θs ) A,

(6)

where h [W/(m2 K)] is the total heat transfer coefficient, A [m2 ] is the heat transfer area of the region, θ n [°C] is the environmental temperature, and θ s [°C] is the surface temperature. The U-value of the regions in a steady state is then obtained using the difference between inside and outside temperatures from the averages of observed values, utilising the following equation [76]:

U=

Q

(θni − θne ) · A

,

(7)

where Q [W] is the heat flow rate, and θ ni [°C] and θ ne [°C] are the inside and outside environmental temperatures of the region to be measured, respectively. The inside surface temperature distribution of the region is measured with an IR camera. The surface temperature of the adiabatic plane material (environmental temperature sensor) placed in proximity to a building element is assumed to represent the indoor environmental temperature, while the total heat transfer coefficient of the surface of the building element is measured using a heat transfer coefficient sensor. In a case where the surface temperature of the measured region varies, the average temperature should be used considering the temperatures of each area of the region. To achieve accurate results, direct influences from fans and heating or cooling devices should be avoided, the measurement area should be free of all visual interference that impedes the field of view for the IR imager (such as curtains, wall hangings, and plants), and the difference between the inside and outside temperatures must be greater than 10 °C when the heater is on [18,74,76,83,102]. To eliminate temperature variations, the inside of the region to be measured must be completely sealed by closing

– Because of outside weather conditions and effects such as local shade, solar radiation, and wind velocity, only the internal surface temperature is observed with an IR camera. – Measurements are only taken at night because outdoor air and room temperatures are relatively stable, and the temperature is not affected by sunlight and heat transmission through building elements. – When the U-value is low, the heat flow of the building element is also low, which makes it difficult to ensure that measurements are reliable. In addition to the limitations defined in the standard, some authors have also clarified disadvantages based on research conducted, and these include the need for complete evacuation of the building [73], cost of the equipment used [104], need for qualified experienced personnel [105], and difficulty in providing thermal stimulation over a large surface [104]. To date, a considerable amount of research in the field of IRT has been conducted for building diagnostics, and several review papers have been published. In 2002, Balaras and Argiriou [106] presented in detail the main areas in which IR is employed in building diagnostics. In addition, Kylili et al. [107] reviewed state-of-the-art literature and research based on passive and active IRT, and presented the fundamentals of IRT and thermographic processes used in building diagnostics. The authors stated that even though IRT is a useful tool, more advanced and accurate approaches could be developed. Meola et al. [108] provided an overview of how an IR imaging device can be used to obtain information about the conditions of a given material or structure. The authors described the main non-destructive thermographic techniques, testing procedures, testing parameters, and image processing involved. Furthermore, Nardi et al. [109] described the IRT technique and its development throughout the years, and presented several recurring energy-related problems together with perspectives obtained from the building sector; they remarked on the need for specialised thermographers to deal with an evolving methodology. Kirimtat and Krejcar [110] presented a detailed review of IRT in the investigation of building envelope defects. An extensive review of the IRT method with respect to energy audits of buildings was presented in a recently published paper by Lucchi [111], in which previous research of IRT measurement results were found to be similar to design U-values; however, they were lower than those obtained using the HFM method. Soares

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

10

et al. [112] used the results of previous studies and associated knowledge to describe the standard framework, and to highlight the main advantages and disadvantages of using the IRT method to assess the U-value of construction elements. To evaluate the accuracy of the IRT method measurement procedure, a summary and overview of previous research conducted on different wall types is provided in Table 3, together with a brief list of experimental methods employed and discrepancies in results, with respect to either the calculations or HFM measurements results. The presented deviation between the results obtained with NCaR method and theoretical U-value or the one obtained by using HFM method or Guarded Hot Box (GHB) is calculated using the following expression:

UIRT − UC,

H F M or

   UIRT − UC, HF M or GHB    ∗ 100, GH B =   UC, H F M or GH B

(8)

In the presented research overview, the IRT method has been used to measure the U-value of walls both inside [83,117,121,122] and outside [16,74,103,118]. According to research conducted to date, the accuracy of using IRT measurements in Uvalue assessments can be affected by several parameters, including emissivity [49,83,104,105,123,124], reflectivity [49], colour [49], solar irradiation [74,103], wind [74,103], IR-radiation of the environment [103], proper interpretation of surface heat transfer coefficient [115,125–128] and the difference between inner and outer temperatures [74]. According to studies, convective heat transfer coefficient has a significant influence on the results [115]. Therefore, when conducting IRT measurements a real value of convective heat transfer coefficient should be measured and used and not a standard value [115]. More studies based on the IRT approach for U-value measurements of different building elements can also be found in literature, such as those relating to measurements conducted on roofs [83,129] and glass [83,130]. 3.2. Temperature based method TBM is a relatively new and simple non-standardised method used to conduct in-situ measurements of the U-value. In literature, the method is also referred to as the thermometric method (THM) [131] or air–surface temperature ratio (ASTR) method [132]. The measurement procedure for the temperature based method requires monitoring of only three temperatures: inside temperature (Ti ), outside temperature (Te ), and inside wall surface temperature (Tsi ). Therefore, two temperature sensors, a data logger, and a surface temperature sensor are the main instruments used to conduct measurements. The methodology behind this method is based on Newton’s law of cooling, which states that the heat transfer rate is proportional to the temperature difference between a body and its surroundings and the area of the surface [133]. In a steady state, Newton’s law of cooling is expressed as:

Q = hA(Ts − T ),

In terms of the U-value, the heat flow rate through a building envelope is equal to:

Q = AU (Ti − Te )

(11)

therefore, the U-value can be determined based on the three temperature measurements using the following equation:

U = hi

Ti − Tsi , Ti − Te

(12)

where Te [K] is the external air temperature. Known commercial systems that calculate the U-value considering internal and external air temperature, inside wall surface temperature, and surface heat transfer coefficient typically use the fixed design value of the internal surface heat transfer coefficient provided in ISO 6946:2017 [134]. However, this value represents only an estimation and is not necessarily representative of real heat transfers between the environment and wall [135]. Therefore, to obtain a more reliable result, it is crucial that the surface heat transfer coefficient is determined by measuring physical parameters. As with the other non-destructive methods, TBM is also based on establishing a temperature gradient, and the minimal temperature difference when performing the measurement should be at least equal to 15 °C [131]. Furthermore, it is recommended that measurements are conducted in constant conditions, avoiding temperature fluctuations between inside and outside temperatures which can influence the measuring results, and that measuring data are recorded for post-processing. The main difference between TBM and the standardised HFM method relates to the way that the heat flux is determined. When conducting the HFM method, all of the parameters required to obtain the U-value are measured directly (i.e. the heat flux is obtained from a heat flux sensor attached to the wall surface that is being examined, and environmental temperatures are measured using two temperature sensors); whereas when TBM is employed, the heat flux is approximated by measuring the inside, outside, and inside wall surface temperatures (Ti , Te , and Tsi , respectively) as shown in Fig. 5. One of the first studies found in literature using TBM was per´ c´ et al. [136]. The authors evaluated formed in 2009 by Vucˇ icevi a simulation of the thermal performance of a residential building with four floors in the suburb of Belgrade. Measurements of inside and outside air temperature and the U-value of the apartment envelope were conducted using the TBM over four days in December, and results were compared with simulation results for a similar weather period. Cuerda et al. [22] studied the performance gap (i.e. the difference between the measured and simulated thermal performance of two residential buildings in use) using TBM to determine the U-value of façades and compared the buildings’ performances through on-site monitoring and simulation modelling

(9)

where Q [W] is the heat flow rate, h [W/(m2 K)] is the surface heat transfer coefficient, A [m2 ] is the surface area, Ts [K] is the body surface temperature, and T [K] is the surrounding temperature. With respect to simultaneous heat transfer, the total heat transfer is determined by considering the contributions of each heat transfer mechanism. In terms of a building wall and considering that Ti >Te , Newton’s law of cooling can be expressed as:

Q = hi A(Ti − Tsi ),

(10)

where hi [W/(m2 K)] is the internal surface heat transfer coefficient, Ti [K] is the indoor air temperature, and Tsi [K] is the indoor surface temperature.

Fig. 5. Schematic diagram of TBM.

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

11

Table 3 Summarised review of IRT studies on U-value assessment of building walls.

Author and Year

Methods

Kato et al. 2007 [113]

- IRT - GHB - ISO 8990 - Calculated method (Uc )

Albatici and Tonelli 2008 [54]

Type of wall/wall materialYear of construction

n

Deviation [%]

Test period

- Wooden wall filled with glass wool - n.a.

2

2–7 Nov 2006

- IRT - Calculation method (Uc ) - HFM method

- Light external walls - n.a

3

Albatici and Tonelli 2010 [74]

- IRT - Calculated method (Uc ) - HFM method

- 2 light and 1 heavy external wall - n.a.

3

Grinzato et al. 2010 [114]

-

- Insulated external wall - n.a.

1

5.5% IRT-Uc 4% IRT-GHB 31% IRT-Uc 17% IRT-HFM 27–31%(162%∗ ) Average 30% IRT-Uc 17% IRT–HFM 1%–37% IRT-Uc

- Brick, stone and insulated masonry walls - 1920, 1971, 1989, 2004, and 2008

5

- IRT - Calculation method (Uc ) – using three different external heat transfer coefficient values - IRT - Calculation method - ISO 6946:2007

- Solid brick, cavity, insulated cavity, insulated honeycombed blocks wall - 1800, 1960, 1970, 1980, 1997, 2003, 2006, and 2009 - Brick and clay mineral wool insulated - n.a.

14

- IRT - HFM method - IRT - Calculation method (Uc ) - UNI EN ISO 6946:2008 - HFM method - IRT - HFM method

- Laminated timber wall - n.a. - Timber, brick, internally and externally insulated brick wall - n.a. (experimental building)

1

- Perforated bricks with an insulating layer of polystyrene - 1970s; refurbished

1

Nardi et.al. 2015 [51]

- IRT - Calculation method (Uc ) - ISO 6946:2007 - HFM method

3

Nardi 2016 [119]

- IRT – methods proposed by [16, 83, 115, 120] in a Guarded Hot Box (GHB) - Calculation method (Uc ) - HFM method

- Stone, cement-wood brick internally insulated, hollow brick with air layer, insulating panel and curtain wall - Second half of 1800, early 90 s and 2011 - Insulated brick wall - Wall reproducing the typical Italian building stock of the 1970s

Tejedor et al. 2017 [121]

- IRT - Calculation method (Uc ) - UNE-EN ISO 6946:2012 - Notional value (Ut )- UNE-EN ISO 10,456:2012 - HFM method

- Single-leaf wall and multi-leaf wall - 1979 and 2006

2

Tejedor et al. 2018 [122]

- IRT - Calculation method (Uc ) - UNE-EN ISO 6946:2012

- Heavy multi-leaf walls

4

Fokaides and Kalogirou 2011 [83]

Dall’O et al. 2013 [115]

Tzifa et al. 2014 [116]

Danielski and Fröling 2015 [117] Albatici et al. 2015 [16]

Nardi et al. 2014 [118]

∗

IRT Calculation method (Uc ) HFM method IRT Calculation method (Uc ) Thermohygrometer HFM method

1

10

1

Dec 2007–Jan 2008; 23 Jan 2008

Jan and Feb

15 Mar 2010

0%–12% Average 9% IRTwinter -Uc 1%−24% Average 13% IRTsummer -Uc 1.5%–154% ∗∗ Average 36% IRT-Uc

Summer and winter, Aug 2009 and Feb 2010

2%–68% Average 29% IRTwinter -Uc 10%−286% Average 97% IRTsummer -Uc 3%–11% IRT-HFM 8%–19% Average 14% IRT-Uc

4 Feb 2011 and 21 July 2011

16%–28% Average 22% IRT-Uc 2%–37% IRT-HFM 4%–46% Average 20% IRT-Uc 1%–48% IRT-HFM 0%–96% Average 22% IRT-Uc 0%–77% IRT–HFM 4%–20% (2%−3%∗∗∗ ) Average 12% IRT–Uc 13%−39% (11%∗∗∗ ) IRT-HFM 0.2%–9% Average 4% IRT-Uc

Jan 2013

Jan and Feb Nov 2010–Mar 2011; Nov 2011–Mar 2012; Nov 2012 -Mar 2013 Feb 2013

Meas. campaigns of 72 and 144 h

22 Feb and 27 Feb 2013

Jan and Feb 2016

Jan–Feb 2017

Deviation of 162% was obtained for a wind speed of v = 1 m/s. The percentage absolute deviation was found to be out of acceptable level (being more than 50%) for buildings insulated externally. ∗∗∗ Deviations of 4%–20% and 13%−39% were obtained performing the measurement without heating the building while deviations of 2%−3% and 11% were obtained executing the test with the heating system switched on 48 h previously. ∗∗

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

12

that were independent of the occupants’ behaviour. Buzatu et al. [137] conducted a comparison of U-values obtained using the TBM measuring procedure and a theoretical calculation methodology in accordance with MC0 01/ 20 09, which considered the influence of thermal bridges characterised by an overall reduction in the unidirectional thermal resistance. The authors conducted measurements on two exterior walls and obtained percentage differences of up to 44.19% and 40.18% between theoretical and measured U-values when using TBM. The authors stated that these differences could be owing to the unknown stratigraphy of the inner part of the wall, or to an inaccurate thermal conductivity value that was selected for the component materials. As it can be seen, until recently TBM had mostly been used to obtain U-value measurements, and that deeper analyses of the reliability and applicability of the method had not been conducted. However, several recent studies have now been conducted to address these issues. For example, to improve the current methodology based on heat flow measurements, Andújar Márquez et al. [138] developed a wireless thermal transmittance metre based on the three temperatures measurements methodology that performs in real time and provides automatic adjustment of the test duration. In their study, the authors compared measured U-values obtained using the developed system with those from a standardised HFM method to evaluate system accuracy and reliability. The results showed a negligible difference of less than 2% between the U-values obtained by the two methods when they were applied simultaneously. The authors stated that the developed U-value metre provides a series of features that make it ideal for application in building energy retrofitting, as it enables thermal transmittance measurements in buildings to be conducted in an inexpensive, quick, reliable, and simple manner. Another study conducted by Bienvenido-Huertas et al. in Spain [131] analysed the viability of applying the thermometric method (THM) through on-site monitoring during winter, summer, and autumn of eight walls from different building periods. The authors found it difficult to obtain valid results in warmer seasons; however, the values obtained under winter environmental conditions were considered valid with relative uncertainties between 6% and 13% when compared to estimated values. Furthermore, to verify the accuracy of the ASTR method, Kim et al..[132] conducted field measurements on four existing houses, both before and after installation of retrofit insulation, by applying and comparing results of HFM method and the ASTR method. The results showed a low relative error rate between the two measurements of 3.32% and 3.09% before and after the retrofit, respectively. In addition, the authors compared the results of long-term (seven days) and short-term (one day from 02:0 0 to 06:0 0) measurements. The obtained average error rate of approximately ±2.63% showed that the ASTR method is capable of providing short-term measurements. In another study, Kim et al. [139] determined the feasibility of improving the in-situ measurement accuracy of the ASTR method. The authors compared measurement results obtained by the HFM and ASTR methods, and results confirmed that the indoor surface heat transfer coefficient and temperature fluctuations increase the uncertainty of the measured U-value. A summarised overview of the method presented in this chapter with deviations of results declared is given in Table 4. The presented deviation between the results obtained with TBM (THM or ASTR method) and theoretical value or U-value obtained by HFM method is calculated using the following expression:

UT BM − UC

or HF M

  UT BM − UC

=

UC

or HF M

or HF M

   ∗ 100,

(13)

Although TBM is often used in practice, it lacks accuracy evaluation, since only few researchers have studied the actual behaviour of the built wall in real conditions using this method. Furthermore,

Fig. 6. Schematic diagram of NCaR method.

there is a lack of research involving detail analysis and measurement of surface heat transfer coefficient as a crucial parameter in this method. However, from the presented overview of TBM studies on U-value assessment of building walls it can be seen that method provides promising results and should therefore be a subject of future studies due to its advantages as being quick, simple and less expensive compared to HFM method. 3.3. Natural convection and radiation method The NCaR method is another non-standardised method that was proposed to overcome problems associated with the HFM method [140]. The experimental methodology of the NCaR method for the on-site evaluation of thermal transmittance is based on measurements of inside and outside air temperatures, the inner wall surface temperature, and emissivity of the inner wall surface using an IR camera, as shown schematically in Fig. 6. The method requires continuous monitoring of temperatures, and the U-value can be obtained by [140]:

U=

C·

 j

(Ti − Tsi )nj +1 + ε · σ · 

j

(Ti − Te ) j



 j

Ti4 − Tsi4



j

,

(14)

where C and n are constants in the standard form of the convective coefficient expression, Ti [K] is the internal air temperature, Tsi [K] is the temperature of the wall surface, ε is emissivity, σ is the Stefan-Boltzmann constant (σ = 5.6710−8 [W m−2 K−4 ]) and Te [K] is the external air temperature. Although several correlations are available in literature for quantifying the convective heat transfer coefficient with parameters C and n, because of the influence on thermal transmittance, and therefore the building energy consumption, surface convective, and radiative heat transfer coefficients should be determined by measuring physical parameters [135]. To provide reliable results, the NCaR method requires stationary measurement conditions within an acquisition time of at least 72 h at a minimum temperature difference of 10 °C between the inside and outside air. The recording interval is the same as for the HFM method, and forced convection should be avoided during U-value measurements; natural convection is assumed to be the only type of convection heat transfer from the indoor air to the inner wall surface. The NCaR method was proposed by Jankovic´ et al. [140] in 2017, when they conducted HFM measurements using the progressive mean method and compared the measured U-values to the ones obtained by using the NCaR method. The authors also compared results obtained using different expressions for the convective heat transfer coefficient available in literature. The results of their study

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

13

Table 4 Summarised review of TBM (THM or ASTR method) studies on U-value assessment of building walls. Author and Year

Methods

Type of wall/wall materialYear of construction

n

Deviation [%]

Cuerda et al. 2016 [22]

- Solid brick with 4 cm insulation - Built in 1972, renovated in 2011

1

31% TBM-UC

Seven days in Summer

- Insulated walls - n.a.

2

40%,44% TBM-UC

n.a.

Andújar Márquez et al. 2017 [138]

- TBM - Calculation – Spanish regulation and databases - TBM -Theoretical calculation - MC001/ 2009 - TBM - HFM method - ISO 9869-1

1

2% TBM-HFM

Summer

Bienvenido-Huertas et al. 2018 [131]

- THM - Calculation method - ISO 6946

- Wall with externally insulated finish system (EIFS) - n.a. - 5 insulated and 3 non-insulated multilayer brick walls - 1966, 1981, and 2004

Winter, Summer and Autumn

Kim et al. 2018 [132]

-

4%−37% THMwinter -UC 7%−62% ∗ THMsummer -UC 19%−83% ∗ THMautumn -UC 0.3%−5% ASTR-HFM 6%−17% ASTR-HFM

Buzatu et al. 2017 [137]

Kim et al. 2018 [139]

ASTR In-situ HFM method - ISO 9869-1 ASTR HFM method - ISO 9869-1

-

Multilayer brick walls 1978, 1979, 1989, and 1991 Insulated brick walls 1982, 1983, 1988, and 1994

8

∗

4 12

Test period

1 Nov 2015–31 Dec 2015 15 Nov 2017–15 Dec 2017

∗ Presented deviations include measurements with two data loggers for which authors obtained significant differences between the results. The main cause of the existing differences between the results was caused by the variations presented by the measurements of indoor and outdoor air temperatures recorded by both sets of equipment. Optimal behavior was obtained in winter with one of the data loggers used, with relative uncertainties between 6% and 13%.

Table 5 Summarised review of study based on NCaR method for U-value assessment and associated results. Type of wall/Wall materialYear of construction

Author and Year

Methods

Jankovic´ et al. [140]

- NCaR method – using numerical values of C and n constants derived from the expression for convective heat transfer coefficient defined by several authors [141-147] and standard [148] - Calculation method (Uc ) - HFM method

- Plastered hollow brick wall - n.a.

showed a very good agreement between measured U-values obtained with both methods, and the deviation ranged from 5%–29%. The authors stated that the NCaR method does not leave marks, nor does it damage the surface of a building element, and it is as equally reliable as the standard HFM method; however, it is significantly simpler and less expensive. The authors also outlined another meaningful advantage in comparison with the HFM method: the possibility of measuring light building walls. This is because the sensors used to measure temperature do not significantly modify the heat flow and temperature field on the surface of a building element. Table 5 summarises research results of measurements conducted using the NCaR method. The presented deviation between the results obtained with NCaR method and theoretical U-value or the one obtained by using HFM method is calculated using the following expression:

UT BM − UC

or HF M

  UT BM − UC

=

UC

or HF M

or HF M

   ∗ 100,

(15)

Looking on the experimental methodology of the NCaR method it can be compared to the experimental methodology of TBM since the only difference lies in the determination of surface heat transfer coefficient. The difference is that in NCaR method additional measurements need to be performed in order to obtain a real value of surface heat transfer coefficient that can be different from the one proposed in standard. Since NCaR method has only been tested by the authors of the method it needs further testing on different types of building elements and in different conditions in order to define limitations, to eliminate possible negative impacts and to draw more profound conclusions about the method reliability.

n

Deviation [%]

Test period

1

5%–29% Avg.12% NCaR–HFM 0.5%−26% Avg. 9% NCaR–Uc

10 Feb–13 Feb 2015

4. Comparison between most significant existing in-situ methods A comparison between methods made with respect to accuracy, cost, instrument setup, and time of execution is presented in Table 6. Based on the comparison, the complexity of each method can be derived. Factors on which comparison was made are explained in detail below. The accuracy of each method is evaluated based on published deviations in a range i.e., the minimum and maximum deviations are presented in Table 6. It has been shown that the accuracy of all methods can be affected by various factors such as operative measurement conditions and characteristics of the envelope component which can strongly affect in situ U-value [46], and some of the reported deviations are actually the result of these influences. During summer, it has been shown that it is harder to achieve minimum temperature difference for conducting measurements [131] and special attention should be given to wall orientation. The major influencing factors on the external wall surface temperature of buildings have been investigated by Lehmann et al. [103]. Based on measurements at a test building and a subsequent sensitivity study by simulation it has been shown that the behaviour of an external wall surface strongly depends on the wall assembly itself i.e., the thermal properties of materials used and that solar- and IR-radiation pose the strongest restrictions in terms of thermography as their perturbations dies out slowest [103]. Authors also stated that the higher the mass exposed to the sun, the more solar energy is stored in the near-surface layers and the longer the solar influence is traceable, even after sundown [103].

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

14

Table 6 Comparison between methods presented in this paper with respect to accuracy, cost, time of execution, and instrument setup. Method

Accuracy

Cost

Time

Instrument setup

Criteria HFM IRT TBM NCaR SHB-HFM

Range [%] 0%–163% (45%−142%∗ )Avg. 24% (90%∗ ) 0%–162% (1%−286%∗ )Avg. 19% (55%∗ ) 4%–37% (2%−62%∗ )0.3%−17%∗∗ 0.5%–26%5%−29%∗∗ 4%–7%

Low/High High High Low Low High

Long/Short Min. 3 days Min. 3 nights Less than 1 day Min. 3 days n.a.

Simple/Complex Complex Complex Simple Simple Complex

∗ ∗∗

Deviations obtained for measurements performed in summer conditions. Percentage difference compared to U-values obtained using HFM method.

In addition, for the HFM and IRT methods reported deviations are thus given for summer and winter condition separately together with average value. It should be taken into account the fact that for some methods several studies have been performed and for other only limited number of studies is available. Therefore, presented results indicate obtained accuracy in conducted studies and not accuracy of the methods. For all methods, deviations are given with respect to the analytically calculated value. Deviations for the TBM and NCaR methods are also given compared to HFM method results, as new and alternative methods have mostly used the HFM method as a comparison. Regarding the cost, the costs of the equipment, installation material, and staff training are evaluated. For example, for the IRT method, three levels of competencies are defined for qualifying IRT personnel for the building diagnosis and energy audit [149]. For time of execution, the minimum defined period found or prescribed in literature is given. Finally, instrument setup is evaluated as being either simple or complex in terms of the required equipment and associated limitations of use, such as building users interruptions and the time required for instrument setup (prior to use and on site). In order to provide more accurate model for assisting researchers to select an appropriate measurement procedure to employ depending on parameters such as the type of a wall (light,heavy…), season, measurement time, complexity, data postprocessing methods and research purpose (certification, audit…) it is necessary to conduct further research for all existing methods, especially for new, non-standardised methods analysed in this paper. Based on available and additional research it will be possible to provide a flow-chart supporting practitioners’ decision. 5. Conclusion Currently, it is extremely important to be able to estimate the actual energy performance of buildings, and thus enable the identification of appropriate and reasonable energy-saving measures. The objective is to increase the energy performance of existing buildings during energy renovation, or to incorporate measures into the building designs of new buildings to ensure that they comply with current energy policies aimed at nearly-zero energy building stock. Thus, the development and improvement of existing as well as new methods for in-situ assessment of the actual thermal performance of building elements is lately a subject of increasing attention. The main objective is to provide a reliable, simple to operate and process, easy to transport and quick to apply, inexpensive, and non-destructive method applicable for conducting building inspections and energy audits in occupied dwellings. It is important to emphasise that when developing and improving in-situ measurements, it is necessary to explore possible influential factors, and it is also necessary to increase accuracy while maintaining the simplicity of operational principles. Several authors have introduced new methods aimed at improving the accuracy and reliability of in-situ measurements of the U-value of

building components, as well as shortening the time span necessary for reliable assessment (even under summer conditions). This paper presents an overview of the current experimental in-situ non-destructive approaches used to determine the U-value of walls. An in-depth report is presented on the use of the HFM, IRT, TBM, NCaR, and SHB-HFM methods. In addition, information is provided about the measurement methodologies employed and the most influential factors that affect the accuracy of each measurement procedure. Papers related to the selected methods are briefly presented. This paper also provides information about the complexity, accuracy, cost, and time of execution of current experimental in-situ approaches, which is useful for energy auditors when making decisions about which measurement procedure to use. Throughout this paper, after each method is discussed, tables are presented to summarise the results and conclusions relating to its use. Although new approaches for in-situ assessment of thermal transmittance have proven to be very effective and promising, based on preliminary results, there is still a lack of comprehensive research to enable more profound conclusions to be drawn about the reliability and applicability of such methods. Conversely, numerous in-depth studies have been conducted applying HFM or IRT methods to determine the U-value of building elements, and such efforts have resulted in standardisation of the IRT method in 2018. Nevertheless, challenges still exist owing to the many declared limitations. This study shows that although a considerable amount of research has been conducted to date, for each in-situ non-destructive approach there remains a number of issues to be studied in future research; the main issues are as follows: - Few in-situ measurements have been conducted under summer environmental conditions, and therefore seasonal limitations have not yet been overcome. - It is a necessity to provide a shorter test duration to enable a larger number of measurements to be carried out in a given time. - It is necessary to determine the optimum size of a dataset that considers the duration of measurements taken and frequency of data collection. - There is a lack of research relating to new methods, as presented in this paper; however, studies currently conducted are providing promising results. Further testing on different types of building elements in different conditions is required to minimise or eliminate all possible negative impacts and to determine all parameters affecting the accuracy of measurement such as surface heat transfer coefficient as one of the most influencing. Declaration of Competing Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

significant financial support for this work that could have influenced its outcome. Acknowledgements This paper is a part of the project entitled ‘Evaluation of experimental methods for the determination of the U-value in steady state conditions’ supported by Grant no. 15-09 provided by the Faculty of Civil Engineering and Architecture Osijek. References [1] Directive (EU), 2018/844 of the European Parliament and of the Council of 30 may 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency, Off. J. Eur. Union (2018). [2] P. Biddulph, V. Gori, C.A. Elwell, C. Scott, C. Rye, R. Lowe, T. Oreszczyn, Inferring the thermal resistance and effective thermal mass of a wall using frequent temperature and heat flux measurements, Energy Build. 78 (0) (2014) 10–16 http://dx.doi.org/10.1016/j.enbuild.2014.04.004. [3] E. Lucchi, Thermal transmittance of historical brick masonries: a comparison among standard data, analytical calculation procedures, and in situ heat flow meter measurements, Energy Build. 134 (2017) 171–184 https://doi.org/ 10.1016/j.enbuild.2016.10.045. [4] E. Lucchi, Thermal transmittance of historical stone masonries: a comparison among standard, calculated and measured data, Energy Build. 151 (2017) 393–405 https://doi.org/10.1016/j.enbuild.2017.07.002. [5] S.N. Flanders, In-situ heat flux measurements in buildings: applications and interpretations of results, in: Proceedings of a Workshop Held in Hanover, New Hampshire on 22-23 May 1990, 1991. [6] G. Branco, B. Lachal, P. Gallinelli, W. Weber, Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data, Energy Build. 36 (6) (2004) 543–555 https:// doi.org/10.1016/j.enbuild.2004.01.028. [7] E. Burman, D. Mumovic, J. Kimpian, Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings, Energy 77 (2014) 153–163 https://doi.org/ 10.1016/j.energy.2014.05.102. [8] D. Majcen, L. Itard, H. Visscher, Actual and theoretical gas consumption in Dutch dwellings: what causes the differences? Energy Policy 61 (2013) 460– 471 https://doi.org/10.1016/j.enpol.2013.06.018. [9] P. de Wilde, The gap between predicted and measured energy performance of buildings: a framework for investigation, Autom. Constr. 41 (2014) 40–49 https://doi.org/10.1016/j.autcon.2014.02.009. [10] M. Sunikka-Blank, R. Galvin, Introducing the prebound effect: the gap between performance and actual energy consumption, Build. Res. Inf. 40 (3) (2012) 260–273. [11] L.K. Norford, R.H. Socolow, E.S. Hsieh, G.V. Spadaro, Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model, Energy Build. 21 (2) (1994) 121–131 https://doi.org/10.1016/0378-7788(94)90 0 05-1. [12] D. Majcen, L.C.M. Itard, H. Visscher, Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: discrepancies and policy implications, Energy Policy 54 (2013) 125–136 https://doi.org/10.1016/j.enpol.2012. 11.008. [13] C. Demanuele, T. Tweddell, M. Davies, Bridging the gap between predicted and actual energy performance in schools, in: World Renewable Energy Congress XI, UAE Abu Dhabi, 2010, pp. 25–30. [14] Thermal I nsulation – Building Elements – In-Situ Measurement of Thermal Resistance and Thermal Transmittance – Part 1: Heat Flow Meter Method (ISO 9869-1:2014). [15] Standard UNI 10351, Materiali da costruzione. Conduttività termica e permeabilità al vapore [Construction materials: tThermal conductivity and vapour permeability], 1994. [16] R. Albatici, A.M. Tonelli, M. Chiogna, A comprehensive experimental approach for the validation of quantitative infrared thermography in the evaluation of building thermal transmittance, Appl. Energy 141 (0) (2015) 218–228 http: //dx.doi.org/10.1016/j.apenergy.2014.12.035. [17] G. Desogus, S. Mura, R. Ricciu, Comparing different approaches to in situ measurement of building components thermal resistance, Energy Build. 43 (10) (2011) 2613–2620 https://doi.org/10.1016/j.enbuild.2011.05.025. [18] F. Asdrubali, F. D’Alessandro, G. Baldinelli, F. Bianchi, Evaluating in situ thermal transmittance of green buildings masonries—a case study, Case Stud. Constr. Mater. 1 (0) (2014) 53–59 http://dx.doi.org/10.1016/j.cscm.2014.04. 004. [19] L. Evangelisti, C. Guattari, P. Gori, R. Vollaro, In situ thermal transmittance measurements for investigating differences between wall models and actual building performance, Sustainability 7 (8) (2015) 10388 https://doi.org/10. 3390/su70810388. [20] K. Gaspar, M. Casals, M. Gangolells, A comparison of standardized calculation methods for in situ measurements of façades U-value, Energy Build. 130 (2016) 592–599 https://doi.org/10.1016/j.enbuild.2016.08.072.

15

[21] L. Evangelisti, C. Guattari, F. Asdrubali, Influence of heating systems on thermal transmittance evaluations: simulations, experimental measurements and data post-processing, Energy Build. 168 (2018) 180–190 https://doi.org/10. 1016/j.enbuild.2018.03.032. [22] E. Cuerda, O. Guerra-Santin, F.J. Neila, N. Romero, Evaluation and comparison of building performance in use through on-site monitoring and simulation modelling, in: Proceedings of the 3rd IBPSA-England Conference BSO 2016, Great North Museum, Newcastle, 2016. [23] V. Gori, V. Marincioni, P. Biddulph, C.A. Elwell, Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces, Energy Build. 135 (2017) 398–409 https://doi.org/10.1016/j.enbuild. 2016.10.043. [24] J. Bros-Williamson, C. Garnier, J.I. Currie, A longitudinal building fabric and energy performance analysis of two homes built to different energy principles, Energy Build. 130 (2016) 578–591 https://doi.org/10.1016/j.enbuild.2016. 08.052. [25] A. Prada, F. Cappelletti, P. Baggio, A. Gasparella, On the effect of material uncertainties in envelope heat transfer simulations, Energy Build. 71 (2014) 53– 60 https://doi.org/10.1016/j.enbuild.2013.11.083. [26] A. Ioannou, L.C.M. Itard, Energy performance and comfort in residential buildings: sensitivity for building parameters and occupancy, Energy Build. 92 (2015) 216–233 https://doi.org/10.1016/j.enbuild.2015.01.055. [27] J.B. Siviour, D.A. Mcintyre, U-value meters in theory and practice, Build. Serv. Eng. Res. Technol. 3 (2) (1982) 61–69, doi:10.1177/01436244820 030 0203. [28] D.A. McIntyre, In situ measurement of U-values, Build. Serv. Eng. Res. Technol. 6 (1) (1985) 1–6, doi:10.1177/01436244850 060 0101. [29] M.P. Modera, M.H. Sherman, R.C. Sonderegger, Determining the U-value of a wall from field measurements of heat flux and surface temperatures, Building Applications of Heat Flux Transducers, ASTM International, 1985. [30] Ž. Koški, I. Ištoka, I. Milicˇ evic´ , Klasifikacija elemenata zgrada u funkciji mjerenja zrakopropusnosti, Gra d¯evinar 65 (2013). [31] T. Kalamees, Air tightness and air leakages of new lightweight single-family detached houses in Estonia, Build. Environ. 42 (6) (2007) 2369–2377 http: //dx.doi.org/10.1016/j.buildenv.2006.06.001. [32] M.H. Sherman, R. Chan, Building Airtightness: Research and Practice, 2004 Lawrence Berkeley National Laboratory Report no. LBNL-5335694720. [33] Thermal performance of Buildings – Determination of Air Permeability of Buildings – Fan Pressurization Method (ISO 9972:2006). [34] I. Netinger Grubeša, M. Teni, H. Krstic´ , M. Vracˇ evic´ , Influence of freeze/thaw cycles on mechanical and thermal properties of masonry wall and masonry wall materials, Energies 12 (8) (2019) 1464. ´ [35] P. Kosinski, P. Brzyski, A. Szewczyk, W. Motacki, Thermal properties of raw hemp fiber as a loose-fill insulation material, J. Nat. Fibers 15 (5) (2018) 717– 730, doi:10.1080/15440478.2017.1361371. [36] A. Aittomäki, Determination of the overall heat transfer coefficient of multilayer structures under non-steady-state conditions, CIB Session Working paper, 1972. [37] I.A. Atsonios, I.D. Mandilaras, D.A. Kontogeorgos, M.A. Founti, A comparative assessment of the standardized methods for the in–situ measurement of the thermal resistance of building walls, Energy Build. 154 (2017) 198–206 https: //doi.org/10.1016/j.enbuild.2017.08.064. [38] K. Gaspar, M. Casals, M. Gangolells, Review of criteria for determining HFM minimum test duration, Energy Build. 176 (2018) 360–370 https://doi.org/10. 1016/j.enbuild.2018.07.049. [39] H. Trethowen, Measurement errors with surface-mounted heat flux sensors, Build. Environ. 21 (1) (1986) 41–56 https://doi.org/10.1016/0360-1323(86) 90 0 07-7. [40] X. Meng, B. Yan, Y. Gao, J. Wang, W. Zhang, E. Long, Factors affecting the in situ measurement accuracy of the wall heat transfer coefficient using the heat flow meter method, Energy Build. 86 (2015) 754–765 https://doi.org/10.1016/ j.enbuild.2014.11.005. [41] P.G. Cesaratto, M. De Carli, S. Marinetti, Effect of different parameters on the in situ thermal conductance evaluation, Energy Build. 43 (7) (2011) 1792– 1801 https://doi.org/10.1016/j.enbuild.2011.03.021. [42] P.G. Cesaratto, M. De Carli, A measuring campaign of thermal conductance in situ and possible impacts on net energy demand in buildings, Energy Build. 59 (2013) 29–36 https://doi.org/10.1016/j.enbuild.2012.08.036. [43] K. Gaspar, M. Casals, M. Gangolells, In situ measurement of façades with a low U-value: avoiding deviations, Energy Build. 170 (2018) 61–73 https://doi. org/10.1016/j.enbuild.2018.04.012. [44] A. Ahmad, M. Maslehuddin, L.M. Al-Hadhrami, In situ measurement of thermal transmittance and thermal resistance of hollow reinforced precast concrete walls, Energy Build. 84 (2014) 132–141 https://doi.org/10.1016/j.enbuild. 2014.07.048. [45] C. Hoffmann, A. Geissler, The prebound-effect in detail: real indoor temperatures in basements and measured versus calculated U-values, Energy Procedia 122 (2017) 32–37 https://doi.org/10.1016/j.egypro.2017.07.301. [46] G. Ficco, F. Iannetta, E. Ianniello, F.R. d’Ambrosio Alfano, M. Dell’Isola, U-value in situ measurement for energy diagnosis of existing buildings, Energy Build. 104 (2015) 108–121 https://doi.org/10.1016/j.enbuild.2015.06.071. [47] P. Baker, U-values and traditional buildings, Glasgow Caledonian University, 2011. [48] A. Tadeu, N. Simoes, I. Simões, F. Pedro, L. Škerget, In-situ thermal resistance evaluation of walls using an iterative dynamic model, Numer. Heat Transf. Part A 67 (1) (2015) 33–51, doi:10.1080/10407782.2014.901032.

16

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

[49] R. Walker, S. Pavía, Thermal performance of a selection of insulation materials suitable for historic buildings, Build. Environ. 94 (2015) 155–165, doi:10.1016/ j.buildenv.2015.07.033. [50] G. Litti, S. Khoshdel, A. Audenaert, J. Braet, Hygrothermal performance evaluation of traditional brick masonry in historic buildings, Energy Build. 105 (2015) 393–411, doi:10.1016/j.enbuild.2015.07.049. [51] I. Nardi, D. Ambrosini, T. de Rubeis, S. Sfarra, S. Perilli, G. Pasqualoni, A comparison between thermographic and flow-meter methods for the evaluation of thermal transmittance of different wall constructions, J. Phys. 655 (2015) 012007, doi:10.1088/1742-6596/655/1/012007. [52] M.J. Jiménez, B. Porcar, M.R. Heras, Application of different dynamic analysis approaches to the estimation of the building component U value, Build. Environ. 44 (2) (2009) 361–367 http://dx.doi.org/10.1016/j.buildenv.2008.03.010. [53] V. Gori, C.A. Elwell, Estimation of thermophysical properties from in-situ measurements in all seasons: quantifying and reducing errors using dynamic grey-box methods, Energy Build. 167 (2018) 290–300 https://doi.org/10.1016/ j.enbuild.2018.02.048. [54] R. Albatici, A.M. Tonelli, On site evaluation of U-value of opaque building elements: a new methodology, Passive and Low Energy Architecture (PLEA) Conference, 2008. [55] S. Doran, Safety and health business plan – field investigations of the thermal performance of construction elements as built, BRE, Building Research Establishment Ltd., 20 0 0. [56] C. Rye, C. Scott, The SPAB research report 1 e U-value report, SPAB, 2012 NovemberPublished 2010 and revised.. [57] S. Rhee-Duverne, P. Baker, Research into the thermal performance of traditional brick walls, English Heritage Report, 2013. [58] F.G.N. Li, A.Z.P. Smith, P. Biddulph, I.G. Hamilton, R. Lowe, A. Mavrogianni, E. Oikonomou, R. Raslan, S. Stamp, A. Stone, A.J. Summerfield, D. Veitch, V. Gori, T. Oreszczyn, Solid-wall U-values: heat flux measurements compared with standard assumptions, Build. Res. Inf. 43 (2) (2015) 238–252, doi:10.1080/09613218.2014.967977. [59] C. Roulet, J. Gass, I. Marcus, In situ U-value measurement: reliable results in shorter time by dynamic interpretation of the measured data, in: Thermal Performance of the Exterior Envelopes of Buildings III; ASHRAE Transactions: Atlanta, GA, USA, 1987, pp. 777–784. [60] G. Anderlind, Multiple regression analysis of in situ thermal measurements — study of an attic insulated with 800 mm loose fill insulation, J. Therm. Insul. Build. Envel. 16 (1) (1992) 81–104, doi:10.1177/109719639201600109. [61] U. Norlén, Estimating thermal parameters of outdoor test cells, Build. Environ. 25 (1) (1990) 17–24 https://doi.org/10.1016/0360- 1323(90)90036- Q. [62] I. Naveros, C. Ghiaus, D.P. Ruíz, S. Castaño, Physical parameters identification of walls using arx models obtained by deduction, Energy Build. 108 (2015) 317–329 https://doi.org/10.1016/j.enbuild.2015.09.021. [63] M.J. Jiménez, H. Madsen, K.K. Andersen, Identification of the main thermal characteristics of building components using MATLAB, Build. Environ. 43 (2) (2008) 170–180 https://doi.org/10.1016/j.buildenv.2006.10.030. [64] M.J. Jiménez, M.R. Heras, Application of multi-output ARX models for estimation of the U and g values of building components in outdoor testing, Sol. Energy 79 (3) (2005) 302–310 https://doi.org/10.1016/j.solener.2004.10.008. [65] O. Gutschker, Parameter identification with the software package LORD, Build. Environ. 43 (2) (2008) 163–169 https://doi.org/10.1016/j.buildenv.2006.10.010. [66] P.H. Baker, H.A.L. van Dijk, PASLINK and dynamic outdoor testing of building components, Build. Environ. 43 (2) (2008) 143–151 https://doi.org/10.1016/j. buildenv.20 06.10.0 09. [67] I. Naveros, P. Bacher, D.P. Ruiz, M.J. Jiménez, H. Madsen, Setting up and validating a complex model for a simple homogeneous wall, Energy Build. 70 (2014) 303–317 https://doi.org/10.1016/j.enbuild.2013.11.076. [68] A.-.H. Deconinck, S. Roels, A maximum likelihood estimation of the thermal resistance of a cavity wall from on-site measurements, Energy Procedia 78 (2015) 3276–3281 https://doi.org/10.1016/j.egypro.2015.11.723. [69] I. Naveros, M.J. Jiménez, M.R. Heras, Analysis of capabilities and limitations of the regression method based in averages, applied to the estimation of the U value of building component tested in Mediterranean weather, Energy Build. 55 (0) (2012) 854–872 http://dx.doi.org/10.1016/j.enbuild.2012.09.028. [70] M.J. Jiménez, B. Porcar, M.R. Heras, Estimation of building component UA and gA from outdoor tests in warm and moderate weather conditions, Sol. Energy 82 (7) (2008) 573–587 https://doi.org/10.1016/j.solener.2008.02.013. [71] A.-.H. Deconinck, S. Roels, Comparison of characterisation methods determining the thermal resistance of building components from onsite measurements, Energy Build. 130 (2016) 309–320 https://doi.org/10.1016/j.enbuild. 2016.08.061. [72] X. Meng, Y. Gao, Y. Wang, B. Yan, W. Zhang, E. Long, Feasibility experiment on the simple hot box-heat flow meter method and the optimization based on simulation reproduction, Appl. Therm. Eng. 83 (2015) 48–56 https://doi.org/ 10.1016/j.applthermaleng.2015.03.010. [73] A. Rasooli, L. Itard, C.I. Ferreira, A response factor-based method for the rapid in-situ determination of wall’s thermal resistance in existing buildings, Energy Build. 119 (2016) 51–61 https://doi.org/10.1016/j.enbuild.2016.03.009. [74] R. Albatici, A.M. Tonelli, Infrared thermovision technique for the assessment of thermal transmittance value of opaque building elements on site, Energy Build. 42 (11) (2010) 2177–2183 http://dx.doi.org/10.1016/j.enbuild.2010. 07.010.

[75] M. Fox, S. Goodhew, P. De Wilde, Building defect detection: external versus internal thermography, Build. Environ. 105 (2016) 317–331 https://doi.org/10. 1016/j.buildenv.2016.06.011. [76] Thermal I nsulation – Building Elements – In-situ Measurement of Thermal Resistance and Thermal Transmittance – Part 2: Infrared Method for Frame Structure Dwelling (ISO 9869-2:2018). [77] S.B. Tian, Study on In-situ Measurement Method of Heat Transfer Coefficient of Building Envelop (Master Thesis), Xi‘an University of Architecture and Technology, Xi’an, 2006. [78] L. Pan, B.M. Chen, Z.H. Fang, B.F. Han, Y.T. Zhen, Measurement of thermal resistance of building enclosures by means of the heat box method, Build. Energy Environ. 2 (2005) 74–77. [79] L. Pan, B.M. Chen, Z.H. Fang, Y.T. Zhen, Field measurement and data processing method of envelope’s thermal resistance, Build. Energy Environ. 6 (2005) 80–84. [80] X.F. Zhu, L.P. Li, X.B. Yin, An in-situ test apparatus of heat transfer coefficient for building envelope, Build. Energy Effic. 256 (2012) 57–60. [81] X. Meng, T. Luo, Y. Gao, L. Zhang, Q. Shen, E. Long, A new simple method to measure wall thermal transmittance in situ and its adaptability analysis, Appl. Therm. Eng. 122 (2017) 747–757 https://doi.org/10.1016/j.applthermaleng. 2017.05.074. [82] M. Scarpa, P. Ruggeri, F. Peron, M. Celebrin, M. De Bei, New measurement procedure for U-value assessment via heat flow meter, Energy Procedia 113 (2017) 174–181 https://doi.org/10.1016/j.egypro.2017.04.050. [83] P.A. Fokaides, S.A. Kalogirou, Application of infrared thermography for the determination of the overall heat transfer coefficient (U-Value) in building envelopes, Appl. Energy 88 (12) (2011) 4358–4365 http://dx.doi.org/10.1016/j. apenergy.2011.05.014. [84] T. Taylor, J. Counsell, S. Gill, Energy efficiency is more than skin deep: improving construction quality control in new-build housing using thermography, Energy Build. 66 (2013) 222–231, doi:10.1016/j.enbuild.2013.07.051. [85] M.D. Gonçalves, T. Colantonio, Commissioning of exterior building envelopes of large buildings for resultant moisture accumulation using infrared thermography and other diagnostic tools, in: Therm. Perform. Exter. Envel. Whole Build. X Int. Conf., 2007, pp. 1–10. [86] C. Lerma, E. Barreira, R.M.S.F. Almeida, A discussion concerning active infrared thermography in the evaluation of buildings air infiltration, Energy Build. 168 (2018) 56–66 https://doi.org/10.1016/j.enbuild.2018.02.050. [87] G. Kilic, Using advanced NDT for historic buildings: towards an integrated multidisciplinary health assessment strategy, J. Cult. Herit. 16 (4) (2015) 526– 535 https://doi.org/10.1016/j.culher.2014.09.010. [88] D.J. Titman, Applications of thermography in non-destructive testing of structures, NDT & E Int. 34 (2) (2001) 149–154 https://doi.org/10.1016/ S0963-8695(0 0)0 0 039-6. [89] J. Hopper, J.R. Littlewood, T. Taylor, J.A.M. Counsell, A.M. Thomas, G. Karani, A. Geens, N.I. Evans, Assessing retrofitted external wall insulation using infrared thermography, Struct. Surv. 30 (3) (2012) 245–266, doi:10.1108/ 02630801211241810. [90] A. Taileb, H. Dekkiche, Infrared imaging as a means of analyzing and improving energy efficiency of building envelopes: the case of a leed gold building, Procedia Eng. 118 (2015) 639–646 https://doi.org/10.1016/j.proeng.2015. 08.497. [91] S.S. de Freitas, V.P. de Freitas, E. Barreira, Detection of façade plaster detachments using infrared thermography – a nondestructive technique, Constr. Build. Mater. 70 (0) (2014) 80–87 http://dx.doi.org/10.1016/j.conbuildmat. 2014.07.094. [92] J.R. Kominsky, J. Luckino, T. Martin, Passive infrared thermography—a qualitative method for detecting moisture anomalies in building envelopes, Tedford Pond 2005 (2007) 1–11. [93] E. Barreira, R.M.S.F. Almeida, J.M.P.Q. Delgado, Infrared thermography for assessing moisture related phenomena in building components, Constr. Build. Mater. 110 (2016) 251–269 https://doi.org/10.1016/j.conbuildmat.2016.02.026. [94] E. Grinzato, P.G. Bison, S. Marinetti, Monitoring of ancient buildings by the thermal method, J. Cult. Herit. 3 (1) (2002) 21–29 https://doi.org/10.1016/ S1296- 2074(02)01159- 7. [95] E. Edis, I. Flores-Colen, J. De Brito, Building thermography: detection of delamination of adhered ceramic claddings using the passive approach, J. Nondestruct. Eval. 34 (1) (2014) 268, doi:10.1007/s10921- 014- 0268- 2. [96] Thermal P erformance of B uildings – Qualitative Detection of Thermal Irregularities in Building Envelopes – Infrared Method (ISO 6781:1983 Modified; EN 13187:1998). [97] F. Bianchi, A. Pisello, G. Baldinelli, F. Asdrubali, Infrared thermography assessment of thermal bridges in building envelope: experimental validation in a test room setup, Sustainability 6 (10) (2014) 7107–7120. [98] M. O’Grady, A.A. Lechowska, A.M. Harte, Infrared thermography technique as an in-situ method of assessing heat loss through thermal bridging, Energy Build. 135 (2017) 20–32 https://doi.org/10.1016/j.enbuild.2016.11.039. [99] M. O’Grady, A.A. Lechowska, A.M. Harte, Application of infrared thermography technique to the thermal assessment of multiple thermal bridges and windows, Energy Build. 168 (2018) 347–362 https://doi.org/10.1016/j.enbuild. 2018.03.034. [100] G. Baldinelli, F. Bianchi, A. Rotili, D. Costarelli, M. Seracini, G. Vinti, F. Asdrubali, L. Evangelisti, A model for the improvement of thermal bridges

´ ski / Energy & Buildings 203 (2019) 109417 M. Teni, H. Krsti´c and P. Kosin

[101]

[102]

[103]

[104] [105] [106]

[107]

[108]

[109]

[110]

[111]

[112]

[113]

[114] [115] [116]

[117]

[118]

[119]

[120] [121]

[122]

[123]

quantitative assessment by infrared thermography, Appl. Energy 211 (2018) 854–864 https://doi.org/10.1016/j.apenergy.2017.11.091. S. Sfarra, A. Cicone, B. Yousefi, C. Ibarra-Castanedo, S. Perilli, X. Maldague, Improving the detection of thermal bridges in buildings via on-site infrared thermography: the potentialities of innovative mathematical tools, Energy Build. 182 (2019) 159–171 https://doi.org/10.1016/j.enbuild.2018.10.017. F. Asdrubali, G. Baldinelli, F. Bianchi, A quantitative methodology to evaluate thermal bridges in buildings, Appl. Energy 97 (2012) 365–373 https://doi.org/ 10.1016/j.apenergy.2011.12.054. B. Lehmann, K. Ghazi Wakili, T. Frank, B. Vera Collado, C. Tanner, Effects of individual climatic parameters on the infrared thermography of buildings, Appl. Energy 110 (0) (2013) 29–43 http://dx.doi.org/10.1016/j.apenergy.2013.03.066. X.P.V. Maldague, Introduction to NDT By Active Infrared Thermography, 2002. D.S. Prakash Rao, Infrared Thermography and Its Applications in Civil Engineering, 2008. C.A. Balaras, A.A. Argiriou, Infrared thermography for building diagnostics, Energy Build. 34 (2) (2002) 171–183 http://dx.doi.org/10.1016/S0378-7788(01) 00105-0. A. Kylili, P.A. Fokaides, P. Christou, S.A. Kalogirou, Infrared thermography (IRT) applications for building diagnostics: a review, Appl. Energy 134 (0) (2014) 531–549 http://dx.doi.org/10.1016/j.apenergy.2014.08.005. C. Meola, S. Boccardi, G.M. Carlomagno, Chapter 4 - Nondestructive Testing with infrared thermography, in: C. Meola, S. Boccardi, G.M. Carlomagno (Eds.), Infrared Thermography in the Evaluation of Aerospace Composite Materials, Woodhead Publishing, 2017, pp. 85–125. I. Nardi, E. Lucchi, T. de Rubeis, D. Ambrosini, Quantification of heat energy losses through the building envelope: a state-of-the-art analysis with critical and comprehensive review on infrared thermography, Build. Environ. 146 (2018) 190–205 https://doi.org/10.1016/j.buildenv.2018.09.050. A. Kirimtat, O. Krejcar, A review of infrared thermography for the investigation of building envelopes: advances and prospects, Energy Build. 176 (2018) 390–406 https://doi.org/10.1016/j.enbuild.2018.07.052. E. Lucchi, Applications of the infrared thermography in the energy audit of buildings: a review, Renew. Sustain. Energy Rev. 82 (2018) 3077–3090 https: //doi.org/10.1016/j.rser.2017.10.031. N. Soares, C. Martins, M. Gonçalves, P. Santos, L.S. da Silva, J.J. Costa, Laboratory and in-situ non-destructive methods to evaluate the thermal transmittance and behavior of walls, windows, and construction elements with innovative materials: a review, Energy Build. 182 (2019) 88–110 https://doi.org/10. 1016/j.enbuild.2018.10.021. S. Kato, K. Kuroki, S. Hagihara, Method of in-situ measurement of thermal insulation performance of building elements using infrared camera, 6th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings-IAQVEC, Citeseer, 2007. E. Grinzato, P. Bison, G. Cadelano, F. Peron, R-value Estimation By Local Thermographic Analysis, SPIE, 2010. G. Dall’O’, L. Sarto, A. Panza, Infrared screening of residential buildings for energy audit purposes: results of a field test, Energies 6 (8) (2013) 3859. V. Tzifa, G. Papadakos, A.G. Papadopoulou, V. Marinakis, J. Psarras, Uncertainty and method limitations in a short-time measurement of the effective thermal transmittance on a building envelope using an infrared camera, Int. J. Sustain. Energy 36 (1) (2014) 28–46, doi:10.1080/14786451.2014.982119. I. Danielski, M. Fröling, Diagnosis of buildings’ thermal performance - A Quantitative method using thermography under non-steady state heat flow, Energy Procedia 83 (2015) 320–329 https://doi.org/10.1016/j.egypro.2015.12. 186. I. Nardi, S. Sfarra, D. Ambrosini, Quantitative thermography for the estimation of the U-value: state of the art and a case study, J. Phys. 547 (2014), doi:10. 1088/1742-6596/547/1/012016. I. Nardi, D. Paoletti, D. Ambrosini, T. de Rubeis, S. Sfarra, U-value assessment by infrared thermography: a comparison of different calculation methods in a guarded hot box, Energy Build. 122 (2016) 211–221 https://doi.org/10.1016/ j.enbuild.2016.04.017. R. Madding, Finding R-values of stud frame constructed houses with IR thermography, Proc. InfraMation 2008 (2008) 261–277. B. Tejedor, M. Casals, M. Gangolells, X. Roca, Quantitative internal infrared thermography for determining in-situ thermal behaviour of façades, Energy Build. 151 (2017) 187–197 https://doi.org/10.1016/j.enbuild.2017.06.040. B. Tejedor, M. Casals, M. Gangolells, Assessing the influence of operating conditions and thermophysical properties on the accuracy of in-situ measured U-values using quantitative internal infrared thermography, Energy Build. 171 (2018) 64–75 https://doi.org/10.1016/j.enbuild.2018.04.011. N.P. Avdelidis, A. Moropoulou, Emissivity considerations in building thermography, Energy Build. 35 (7) (2003) 663–667 https://doi.org/10.1016/ S0378- 7788(02)00210- 4.

17

[124] E. Barreira, V.P. de Freitas, Evaluation of building materials using infrared thermography, Constr. Build. Mater. 21 (1) (2007) 218–224 https://doi.org/10. 1016/j.conbuildmat.2005.06.049. [125] I. Beausoleil-Morrison, Modelling mixed convection heat transfer at internal buildings surfaces, in: Proceedings of Building Simulation, 1999, pp. 313–320. [126] A. Hoyano, K. Asano, T. Kanamaru, Analysis of the sensible heat flux from the exterior surface of buildings using time sequential thermography, Atmos. Environ. 33 (24) (1999) 3941–3951 https://doi.org/10.1016/S1352-2310(99) 00136-3. [127] A. Hagishima, J. Tanimoto, Field measurements for estimating the convective heat transfer coefficient at building surfaces, Build. Environ. 38 (7) (2003) 873–881 https://doi.org/10.1016/S0360-1323(03)0 0 033-7. [128] S. Fohanno, G. Polidori, Modelling of natural convective heat transfer at an internal surface, Energy Build. 38 (5) (2006) 548–553 https://doi.org/10.1016/ j.enbuild.20 05.09.0 03. [129] L. Belussi, L. Danza, I. Meroni, F. Salamone, Energy performance assessment with empirical methods: application of energy signature, Opto-Electron. Rev. (2015) 85. [130] K. Maroy, K. Carbonez, M. Steeman, N. Van Den Bossche, Assessing the thermal performance of insulating glass units with infrared thermography: potential and limitations, Energy Build. 138 (2017) 175–192 https://doi.org/10. 1016/j.enbuild.2016.10.054. [131] D. Bienvenido-Huertas, R. Rodríguez-Álvaro, J.J. Moyano, F. Rico, D. Marín, Determining the U-value of façades using the thermometric method: potentials and limitations, Energies 11 (2) (2018) 360 https://doi.org/10.3390/ en11020360. [132] S.-.H. Kim, J.-.H. Kim, H.-.G. Jeong, K.-.D. Song, Reliability field test of the air– surface temperature ratio method for in situ measurement of U-values, Energies 11 (4) (2018) 803 https://doi.org/10.3390/en11040803. [133] Y.A. Çengel, Heat Transfer: A Practical Approach, McGraw-Hill, New York, 2004. [134] Building C omponents and B uilding E lements – Thermal Resistance and Thermal Transmittance – Calculation Method (ISO 6946:2017; EN ISO 6946:2017). [135] L. Evangelisti, C. Guattari, P. Gori, R. de Lieto Vollaro, F. Asdrubali, Experimental investigation of the influence of convective and radiative heat transfers on thermal transmittance measurements, Int. Commun. Heat Mass Transf. 78 (2016) 214–223 https://doi.org/10.1016/j.icheatmasstransfer.2016.09.008. ´ c, ´ V. Turanjanin, V. Bakic, ´ M. Jovanovic, ´ Ž. Stevanovic, ´ Experimen[136] B. Vucˇ icevi tal and numerical modelling of thermal performance of a residential building in Belgrade, Therm. Sci. 13 (4) (2009) 242–252 https://doi.org/10.2298/ TSCI0904245V. [137] G. Buzatu, F. Stan-Ivan, P. Mircea, L. Manescu, Thermal transmittance determination for different components of buildings, in: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 2017, pp. 227–232. [138] J.M. Andújar Márquez, M.Á. Martínez Bohórquez, S. Gómez Melgar, A new metre for cheap, quick, reliable and simple thermal transmittance (U-Value) measurements in buildings, Sensors 17 (9) (2017), doi:10.3390/s17092017. [139] S.-.H. Kim, J.-.H. Lee, J.-.H. Kim, S.-.H. Yoo, H.-.G. Jeong, The feasibility of improving the accuracy of in situ measurements in the air-surface temperature ratio method, Energies 11 (7) (2018) 1885. ´ B. Antunovic, ´ L. Preradovic, ´ Alternative method for on site evalu[140] A. Jankovic, ation of thermal transmittance, facta universitatis, Ser. Mech. Eng. 15 (2017) 341–351, doi:10.22190/FUME170419017J. [141] H.B. Awbi, A. Hatton, Natural convection from heated room surfaces, Energy Build. 30 (3) (1999) 233–244 https://doi.org/10.1016/S0378-7788(99)0 0 0 04-3. [142] A.J.N. Khalifa, R.H. Marshall, Validation of heat transfer coefficients on interior building surfaces using a real-sized indoor test cell, Int. J. Heat Mass Transf. 33 (10) (1990) 2219–2236 https://doi.org/10.1016/0017-9310(90)90122-B. ˚ [143] M.A. Michejev, Základy Sdílení Tepla, Prumyslové vydavatelství, 1952. [144] W.J. King, The Basic Laws and Data of Heat Transmission, American Society of Mechanical Engineers, New York, 1932. [145] W. Nusselt, Das grundgesetz des wärmeüberganges, Gesund. Ing. 38 (42) (1915) 477–482. [146] R.H. Heilman, Surface heat transmission, Mech. Eng. 51 (1929) 355. [147] G. Wilkes, C. Peterson, Radiation and convection from surfaces in various positions, transactions, ASHVE 44 (1938) 513–520. [148] R. American Society of Heating, E. Air-Conditioning, ASHRAE Handbook: Fundamentals, 2001, ASHRAE, Atlanta, GA., 2001. [149] Performance of Buildings – Detection of Heat, Air and Moisture Irregularities in Buildings by Infrared Methods – Part 3: Qualifications of Equipment Operators, Data Analysts and report Writers (ISO 6781-3:2015).