Energy Conversion and Management 89 (2015) 896–906

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Energy Conversion and Management

journal homepage: www.elsevier .com/locate /enconman

Energy balance calculation of window glazings in the northern latitudesusing long-term measured climatic data

http://dx.doi.org/10.1016/j.enconman.2014.10.0580196-8904/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: tuulemall@gmail.com (T.M. Kull).

Tuule Mall Kull a,⇑, Tõnu Mauring a, Alan Henry Tkaczyk b

a Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estoniab Institute of Physics, University of Tartu, Tähe 4, 50090 Tartu, Estonia

a r t i c l e i n f o a b s t r a c t

Article history:Received 14 April 2014Accepted 27 October 2014

Keywords:Glazing energy balanceGlazing parametersLong-term measured climate dataNordic climateFacade orientationNear-zero energy buildings

This article examines long-term patterns in energy performance of windows. It presents energy balancecalculation results of window glazings in cardinal directions in the Estonian climate representing the Nor-dic climate in general. Measured climate input data of 43 years were used in addition to standard energycalculation years. The energy balance of five different glazing types was compared during the heating per-iod of October to March. Calculations were made according to the EU standard ISO 13 790. It was foundthat southern facing glazings with reduced solar to thermal transmittance ratio higher than 0.63 m2 K/W can have positive energy balance over the sum of the heating period. Two of the selected glazingsexceeded this level and achieved positive energy balance over the heating period in all years. On the otherhand, glazings in the other cardinal directions predominantly were found to experience negative meanenergy balance over the sum of the heating period. In the maximization of energy and economic efficiency,this sends the message to architects that, under certain conditions, optimization constraints are moreimportant in some cardinal directions than in others. Such results support scientifically-informed decisionmaking by practitioners at early design stages, for optimal energy efficiency.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

To achieve near-zero energy buildings, there has been a para-digm shift to concentrate attention at the very early design phasein the conceptual planning of buildings. An important challengeis currently present in this field: although some scientific argu-ments exist to consider various designs, only limited informationhas been presented in a way that is useful to architects in the earlydesign phase. With this paper, the authors seek to provide a clearpractical example architects could use to optimize the design ofbuilding envelope geometry and transparent areas at the very earlydesign phase, as well as in later design phases the selection of win-dow glazings appropriate for local conditions.

The policies to improve the energy-efficiency of buildings arebecoming increasingly stringent. For example, the new Europeandirective 2010/31/EU aims for only nearly zero energy buildingsto be built by 2020 [5]. These are very low energy buildings thatproduce most of the energy they need on-site. To achieve nearlyzero energy balance in practice, the first priority should be to min-imize energy losses and only then should energy gains be maxi-mized. In-depth knowledge is required to design and build

buildings with such minimal net energy use, the main principlesbeing [6]: (1) very high insulation level of opaque constructions,(2) junctions with minimal thermal bridges, (3) excellent buildingquality to ensure air-tightness, (4) ventilation with heat recovery,and (5) very high quality windows. In this article we study theeffect of window quality on energy efficiency, as the windowenergy loss forms a significant part of the building’s energybalance. For example, our research team developed the energy con-cept for the prototype net zero energy building in Põlva, Estonia,and found that losses through windows accounted for 44% of thetotal losses in this very energy-efficient case [7]. Moreover, trans-parent components of the building envelope are the only passivemethod to directly transmit solar energy into the building andtherefore, selection of the appropriate window glazing is veryimportant to positively contribute to the energy balance. In fact,glazings through which the building gains more energy than itloses can be considered as the lowest-cost solar collectors.

Architects have the difficult challenge of seeking a compromisebetween theoretical energy-efficiency parameters and real-lifeconstruction, where materials availability, cost, and time factorsplay a role. It is one of the objectives of this paper to provide addi-tional information to support architects in making scientifically-informed decisions about the orientation of the building, windowsurface area in each cardinal direction, and quality parameters of

T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906 897

glazings for buildings at any location around the globe. Because theclimatic conditions are different at each location, it is important tounderstand the window performance in situ. In recent research, ithas been found that buildings with passive solar design can havelarge energy saving potential especially in the cold climates [8].Moreover, window performance can be one of the strongest factorsaffecting building energy balance as very low energy buildings aremuch more sensitive to solar energy than conventional buildings[9].

Very early in the design phase, every architect has to make aprincipal decision about the share of the glazing in each facade.In the open literature there has been scientific discussion on thepotential benefits of smaller vs. larger windows. Because of thepositive energy balance, increasing the window glazing area candecrease heating energy demand in winter. On the other hand,there are other findings suggesting that reduced southern windowareas should be used. Benefits of smaller windows include lowerheating load, better summer comfort (lower cooling energydemand and cooling load), reduction of daylight glare and cost[10–12]. All these factors also influence the optimal glazing param-eters, which make the architect’s decision even more difficult. Weseek to provide some quantitative insight on this matter by includ-ing only the transparent area of the window and excluding framesin our analysis.

Although significant research has been performed on optimalwindow-to-wall ratios [11,13–23], there are fewer publicationsthat tackle the Nordic climate [20–23]. As mentioned above, theunderstanding of in situ energy performance is essential, as recom-mendations for the often studied milder climates may not apply tothe colder Nordic climates. There are some existing studies [17] toassist architects in choosing the optimal window size in thenorthern latitudes, which are often based on the assumption thatheating outweighs cooling considerations. Some researchers sug-gest creating small windows to decrease the cooling energydemand [20,21], whereas others imply that it is possible to achievea positive energy balance for windows in Nordic climates [21,22]. Itis found that in cold climates, it is optimal to concentrate on highsolar transmittance values (g-values) in southern and low thermaltransmittance values (U-values) in northern directions [22]. Thismeans that architects seeking energy-efficiency must solve anoptimization problem, where the constraints are not always soclear. We seek to address this problem by clarifying some of theseconstraints.

In this article, the energy balance of different glazings in theEstonian climate is calculated. The main aim was to determineunder which conditions it is possible to achieve a positive energybalance for windows during the heating period. We were alsointerested in determining the fluctuations of glazing energy perfor-mance over a long period compared to standard energy-calculationclimate years. Namely, it has been shown that the energy demandof a building varies significantly from year to year and the meanvalue over all the years does not necessarily be close to the valuecalculated based on some standard year [24,25]. We advance pre-vious studies that have calculated the energy balance of glazings[23] with either long-term climate measurements or a differentset of climatic conditions [26,27].

2. Material and methods

2.1. Estonian climate

The Estonian climate represents a typical Nordic climate withlow temperatures during the heating period. For building energycalculations one artificial standard year of local climate is usuallyused. In our country, the Estonian Test Reference Year (TRY) [28]

or the climate year generated by Meteonorm for PassivhausProjektierungs-Paket (PHPP) [3,29] are normally used. The latter isalso used for Passive House certification calculations [30].However, these years have been compiled with the aim of simpli-fying the practical calculations and to characterize the real climate,long-term measured climatic parameters are needed. Therefore, inthis study we used 43 years of temperature and irradiation dataprovided by Estonian Institute of Meteorology and Hydrology(EMHI) [31]. These data were measured and recorded every 3 h(hourly in later years) during the period of 1970–2012 in Tõravere,Estonia (N 58�1505000, E 26�2704100).

Direct solar radiation was measured with the AT-50 actinome-ter and later with the NIP pyrheliometer on a surface perpendicularto the sun rays and converted geometrically to the horizontalplane. Diffuse radiation was measured with different pyranome-ters on the horizontal surface. Total irradiation on the horizontalsurface was obtained by adding these two. The instruments usedfor measurements are described in detail in ‘‘Handbook of Estoniansolar radiation climate’’ [32]. In the current study we are interestedin long-term patterns in window energy performance, and there-fore we use monthly irradiation sums and average temperaturesin our calculations. This simplification implies the underlyingassumption that the building behind the glazings has a very largetime constant, which is mostly correct due to the fact that we con-sider very low energy buildings where massive walls are ofteninstalled to enhance the summer performance [33]. Both themonthly irradiation data and monthly mean of the measured tem-perature are shown in Fig. 1.

The temperature graph in Fig. 1a shows that there is a signifi-cant variation in temperatures during the winter period.Exceptionally large are the standard deviations in January andFebruary, but also at the end of the year and in March [34]. FromApril to October, the monthly average temperatures usually exceedzero degrees Celsius. The long-term mean temperature values are�3:3 �C in December, �5:2 �C in January and �5:9 �C in February.However, long-term average daily minimum values are below�10 �C from November to March [28]. In more extreme years,the temperature falls below �30 �C occasionally, staying fre-quently below �15 �C for several days [31]. Irradiation data, onthe other hand, vary more in the summer and are almost the sameevery year during the period from November to January. However,September, October, February, and March experience more differ-ences from year to year. From November to January the mean irra-diation is usually between 6 and 36 kW h/m2 month. There isabout twice as much irradiation in October and February and fourtimes more in March and September. The months with the leastirradiation and lowest temperatures are the months from Octoberto March. We choose this to be the heating period because duringthese months the energy losses are large and gains are small. Thisoutcome is also in agreement with the local experience. We usefixed length of the heating period instead of calculated periodbecause we aim for general results on energy balance of glazingsand we do not include any concrete building to the calculations.

Windows are usually installed vertically, and we converted theraw irradiation data to vertical surfaces facing four cardinal direc-tions. Several methods have been developed for calculating irradi-ation on inclined surfaces [35–39]. Here, both the direct and thediffuse radiation were converted using the isotropic sky methodalso employed in the ‘‘Handbook of Estonian solar radiation cli-mate’’ [32], which states that direct radiation on vertical surfaceis calculated as

Sv ¼ kS � Sh ð1Þ

where Sh is direct radiation on horizontal surface and kS is conver-sion factor dependent on geographic location and month.

1987

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79

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ctN

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ec

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pera

ture

(o C

)

(a) Temperature

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tO

ctN

ovD

ec

Irrad

iatio

n (k

Wh/

m2

mon

th)

TRY

PHPP

Min

Max

Mean

(b) Irradiation on horizontal plane

Fig. 1. Hourly climate measurements converted to monthly data which is used for calculations in the current paper. Figure (a) shows monthly average temperature for eachmonth and year from 1970 to 2012 (small plus marks). Figure (b) shows monthly irradiation on the horizontal surface for same time-points. Here and further error bars showthe extent of standard deviation, also minimum, maximum and mean values over the years are marked as well as data of standard climate years.

898 T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906

Factors for this conversion for Estonia have been calculatedbased on these data in previous work [32] and are reprinted herefor convenience, in appendix Table A.7. This method also statesthat diffuse radiation is converted to vertical planes by dividingby 2. Following this method, global radiation on vertical surfacesoriented to different directions was calculated and the obtaineddata are shown in Fig. 2.

To have a possibility to compare our data to other authors’results, we plot calculations based on the internationally recog-nized PHPP and TRY data together with the calculations done forthe measured data. It can be seen in Fig. 1 that these data usuallyfall near the average value of the period 1970–2012, and are

1998 19

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(c) West

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t

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Irrad

iatio

n (k

Wh/

m2

mon

th)

TRY PHPP

Irradiation

Fig. 2. Irradiation data converted to vertical surfaces. Figures (a), (b), (c) and (d) showrespectively.

always within the one sigma region (extent of standard deviation)of the measurements. There is only one exception to this rule: theirradiation in April in TRY data is one of the lowest values for thismonth both on the horizontal and vertical surfaces. In Fig. 2 weobserve that after transforming the data the irradiation on the ver-tical surface facing north according to PHPP data is in all monthsmore than a standard deviation smaller than the mean of the irra-diation converted from the measurements; it is even slightly lowerthan the smallest value in March. The main reason for these differ-ences in the spring may be attributed to the difference in albedo indifferent conversion methods, which affects diffuse radiationimmensely. However, the differences in final data are small, and

2007

1978

1998 20

08

1992 19

72

1987 1987

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1988

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1974

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2007

1970

2011

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75 1972 19

96 1985

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70

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89 1992

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(b) East

(d) North

Dec Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep

t

Oct

Nov

Dec

Min Max Mean

on vertical surfaces

monthly irradiation for the surfaces oriented to the south, east, west and north

Table 1Symbols and abbreviations used in the paper.

PHPP The climate year generated by Meteonorm for Passivhaus Projektierungs-PaketTRY Estonian Test Reference Yearg Solar transmittance of glazing (–), calculated according to standard EN 410:1999 [1]Ug Thermal transmittance of glazing (W/m2 K), calculated according to standard EN 673:1999 [2]Gq Ratio g=Ug which describes the glazing (m2 K/W)f Reduction factor of solar transmittance as a product of factors due to inclined radiation (0.85), dirt (0.95) and shading (0.9) [3]fGq Gq ratio reduced by the factor of f: fGq ¼ f � g=Ug (m2 K/W)I Solar irradiation on the vertical surface (kW h/m2 per month), in some cases can be for some other time-stepGt Degree-hours as defined in standard EN ISO 13790:2007 [4]DT Room and ambient temperature difference (K), indoor temperature of 20 �C here used

T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906 899

this is not a significant potential source of error, especially as weare not concentrating on the northern facade.

The energy loss through the glass area is also somewhataffected by the wind speed. However, the wind speeds in Estonianclimate remain very low and therefore, we neglect the impact ofwind speed in our model. This is reasonable also because in the for-est or in the cities the buildings are often protected from wind byother objects.

2.2. Parameters of glazings

In this paper, the energy balance of glazings is analyzed. Thismeans that the energy loss through window frames and thermalbridges are excluded from the calculation. The results are givenper square meter of homogeneous glazing area. Five different glaz-ings were used for analysis. Each glazing is described by the ther-mal transmittance (Ug-value) and solar transmittance (g-value),which are assumed to be calculated according to standards EN673 and EN 410 respectively[1,2]. This is a simplified method ofdescription, but the glazing energy transfer calculated based onthese data has been proven to have acceptable accuracy formonthly calculations and has been widely used before [40,41].The values of these parameters for each glazing analyzed are givenin Table 2. Two of the glazings (G1, G3) were based on the work ofother authors [23]. Three new glazings were selected by theauthors of this article to form a representative cross-section ofglazings currently available on the Estonian market. The parame-ters for the new glazings were chosen as follows: the best availableglazing in Estonia at that moment (G2), glazing at the minimumqualification level for Estonian standards (G5), and one glazinghaving the parameters calculated as the arithmetic mean of theother two new glazings (G4).

The best parameters are regarded as the most suitable in thecontext of heating period in Estonia. This means that the glazingshould act as a very good thermal energy barrier at a great temper-ature gradient (very low U-values), and at the same time shouldenable the best possible passive solar energy use (high g-values).

It is clear that there can be a price difference between the differ-ent glazings. However, our choice of parameters is justified by theresult of the Component Award 2014 which shows that the passivehouse windows with Ug-value down to 0.52 W/m2 K can already beproduced at almost the same price as the standard windows withUg ¼ 1:2 W/m2 K and g ¼ 0:6 [42]. Moreover, often the life-cyclecosts are lower for initially more expensive glazings [43].

Table 2Parameters used for glazings in the current work.

Name Source g Ug g=Ug f � g f � g=Ug

G1 [23] 0.47 0.40 1.18 0.34 0.85G2 New 0.62 0.69 0.90 0.45 0.65G3 [23] 0.60 1.00 0.60 0.44 0.44G4 New 0.52 0.90 0.58 0.38 0.42G5 New 0.42 1.10 0.38 0.31 0.28

2.3. Calculation

The monthly energy balance of a glazing was calculated inemploying the monthly method presented in the EN ISO13790:2007 standard [4] with a utilization factor of 100% at lowoutside temperatures. This utilization factor is appropriate sincein our study we assume a very high heat capacity of the building.

As this paper has its focus on the heating period, we are able toapply this as simplification for the whole year. Moreover, very highutilization factor values have been observed in Estonia in a massiveresidential zero-energy building with good passive solar design[44]. During our experience with real-world projects, it was notedthat the monthly method generally estimates energy balancevalues to be lower than observed; therefore, the energy balancevalues estimated by this method are rather conservative than toohigh.

We use the same energy balance sign analysis method devel-oped by Manz and Menti [23]: Gq ¼ g=Ug is used as the glazing’squality indicator, DT=I is used for climate characterization, and aquotient a is defined as

a ¼ g � I=ðUg � DTÞ ð2Þ

Therefore the glazing’s energy balance is positive only if a > 1. Toinclude shading and other influencing factors we adjust the methodand use fGq ¼ f � g=Ug instead of Gq and therefore a ¼ f � g � I=ðUg � GtÞ. The values for the components of the reduction factor fare shown in Table 1.

Using reduced g-value instead of g itself has two main reasons.Firstly, in this way the calculations convey more realistic informa-tion and secondly, even though the usual solar transmittance val-ues and standard for calculation (including shading, dirt, andother parameters) can change the results calculated for certain fgstill hold.

3. Results

All our calculation results for monthly energy balances of differ-ent glazing types in four cardinal directions are shown in a graphFig. A.10 in appendix. This graph confirms our choice of heatingperiod: from October to March at least one glazing experiencesnegative balances in all directions, whereas from April to Septem-ber only northern glazings have a negative energy balance. Alladditional graphs presented are provided to better understandthese data.

The amplitude, standard deviation and mean value of the calcu-lation results for all five glazings are shown in Fig. 3. The graphshows that for all facades, the energy balance of nearly all glazingsis negative in all years from November to January; the exceptionsare the glazings G1 and G2, which can experience minor positive

South East West North

2.4 −3.5 0.9 −3.5 −9.2

22.4 22.2 27.4 18.8

10.3

1−3.59.11 7.67.01

−4.7−16.3 −9.6 −14.9−19.9

4.8 −4.7 2.4 −4.8−10.5

9.41−5.9−9.3− 2.01−6.0−

−7.4−23.2

−14.3 −21.0−28.0

−1.4−10.7 −5.2 −9.8−14.2

1.12−3.51−3.9− 8.61−2.4−

−5.6−23.3

−12.7−21.4

−31.0

3.5 −7.4 0.7 −7.3−13.7

02−2.31−1.6− 2.41−5.1−

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−20.4

19.5 9.6

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−5.8

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31.8 16.6

3.42.512.62 3.815.22

−0.2 −8.0 −2.5 −7.5−12.9

6.8 2.3 6.8 1.6 −3.5

2.8−5.3−3.1 5.3−8.2

−6.1−19.7−11.4 −18.0−24.5

−3.3−12.2 −7.1 −11.2−15.1

4.81−8.31−1.9− 2.51−5.4−

−9.5−28.6

−18.0 −25.9−33.5

−5.2−14.9 −9.6 −13.5−17.2

−7.1 −20.5−13.2 −18.6−23.7

−9.7−30.4

−18.7−27.6

−36.0

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−6.2 −20.3−12.3 −18.4−24.2

−3.7−20.7

−9.0−19.1

−28.8

1.5 −9.9 −2.6 −9.3−14.6

02−8.21−4.5− 8.31−9.0−

5.2 −2.5 3.9 −2.8 −9.8

20.5 14.2 23.1

11.5 0.2

5.5−37.11 2.45.11

−0.2 −8.0 −2.5 −7.5−12.9

6.8 2.3 6.8 1.6 −3.5

2.8−5.3−3.1 5.3−8.2

−6.1−19.7−11.4 −18.0−24.5

−3.3−12.2 −7.1 −11.2−15.1

4.81−8.31−1.9− 2.51−5.4−

−9.5−28.6

−18.0 −25.9−33.4

−5.2−14.9 −9.6 −13.5−17.2

−7.1 −20.5−13.2 −18.6−23.7

−9.6−30.4

−18.7−27.5

−36.0

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−3.2−19.4

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−28.0

3.1 −8.9 −1.0 −8.4−14.0

3.91−9.11−3.4− 8.21−1.0−

5.8 −1.7 4.7 −2.1 −9.0

22.4 16.6 25.5

13.5 1.8

6.4−2.41.31 6.56.21

−2.4−12.7 −6.3 −11.6−16.8

0.8 −4.8 −1.0 −4.5 −7.9

11−1.7−9.2− 6.7−5.0−

−7.5−22.7

−14.3 −20.6−26.6

−4.2−13.3 −8.2 −12.1−15.9

−5.4 −16.4−10.3 −14.9−19.3

−10.4−29.7

−19.2 −26.9−34.2

−5.7−15.5−10.3 −14.0−17.6

−7.6 −21.1−13.8 −19 −24.1

−10.7

−31.7−20.1

−28.7−36.9

−5.6−16.7−10.5 −15.1−19.4

−7 −21.3−13.4 −19.3 −25

−5.2−24.1

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−31.2

−1.4−12.1 −6.0 −11.1−15.5

22−3.51−3.8− 7.61−1.3−

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9.5 1.8 9.1 0.9 −7.1

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−45−30−15

015304560

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015304560

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015304560

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015304560

Oct

Nov

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JanFeb

Mar

G1 G2 G3 G4 G5 G1 G2 G3 G4 G5 G1 G2 G3 G4 G5 G1 G2 G3 G4 G5

Glazing type

Ene

rgy

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nce

of th

e gl

azin

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Wh/

m2m

onth

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TRY PHPP Min Max

Energy balance of different glazing types

Fig. 3. The energy balance calculation results for all five window glazings in each month of the heating period. Boxplot shows minimum, maximum values, mean value andstandard deviation extent. Minimum, maximum, and standard year values are marked separately. The gray region shows where is the positive energy balance region.

900 T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906

balance in the southern facade. The mean value of the balanceremains mostly negative also in February, however in southernfacade glazing G1 achieves positive balances in all years. The samecan be noticed for these glazings in October, and for G1–G4 inMarch. What is more surprising, it is also true for the glazings G1and G2 facing east or west in March. The glazing G5 has mostlynegative energy balance everywhere but even that glazing haspositive balance values for southern facade in October and March.The southern facade is the most different from others. Here theyear-to-year differences are more significant and the energy bal-ance values are much higher in October, February and March thanin other directions.

In the same figure also calculations made on standard years areshown. In most cases the energy balances calculated for themremain in the one sigma region of other calculations or very closeto it. However, in March the glazings facing north have even lowerenergy balance values calculated for the PHPP year than the

minimum of other calculations. In January and February the energybalance of the same standard year is remarkably higher than themean value of calculations.

Our calculations achieve the result that from the selected glaz-ings, only the two of the glazings with the highest g–Ug ratio valuesand only in the southern facade can achieve a positive energy bal-ance as the sum of the heating period in all years Fig. 4 shows. Itcan be also seen in eastern and western directions that the glazingscan have a positive energy balance in some years, but the absolutevalue remains close to zero.

The clear indication of decrease of the energy balance propor-tional to the glazing number (which is in the order of Gq-value)on this graph provokes to carry out a correlation analysis. Theresults for this are shown in Table 3 and it is clear that the meanvalue of the energy balance over the heating period (B) dependsmainly on the thermal transmittance of the glazings. On the south-ern facade, solar transmittance value affects the energy balance

2.5

−5.4

0.4

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11.6

5.8

12.4

4.4

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7.7−9.0−6 3.0−9.6

−1.0

−11.1

−4.1

−10.3

−16.3

2.5

−5.5

0.4

−5.3

−11.0

4.31−8−4.2− 5.8−5.0

−0.8

−10.7

−3.9

−10.0

−16.0

3.1

−4.8

1.2

−4.7

−10.5

1.31−6.7−2− 1.8−8.0

−3.6

−13.8

−7.8

−12.6

−18.0

−0.7

−9.7

−3.9

−9.0

−13.6

5.51−6.01−6.5− 5.11−9.1−

South East West North

−24

−16

−8

0

8

16

G1 G2 G3 G4 G5 G1 G2 G3 G4 G5 G1 G2 G3 G4 G5 G1 G2 G3 G4 G5

Glazing type

TRY PHPP Min Max

Energy balance of different glazing types

Ene

rgy

bala

nce

of g

lazi

ng2m

onth

)(k

Wh/

m

Fig. 4. The energy balance calculation results for all five glazings in the heating period.

Table 3Correlation coefficients between the energy balance behavior over the heating periodin each direction and characteristics of the glazings.

South East West North

B DB B DB B DB B DB

fg 0.45 0.98 0.27 0.22 0.28 0.36 0.20 0.14Ug �0.90 �0.10 �0.97 0.96 �0.96 0.92 �0.98 0.90fGq 0.94 0.23 0.98 �0.91 0.98 �0.86 0.99 �0.84

1992; 2.5

2005; 11.6

Mean = 6.9 Mean = 0.8

South

−4

0

4

8

12

Ene

rgy

bala

nce

(kW

h/m

2 mon

th)

aaa aaa aaa aaaTRY PHPP Min M

Glazing type G1: g=

1985; −11.4

2011; −3.1Mean = −7.7

19

Mean = −13.

South W

−20

−16

−12

−8

−4

0

1975 1985 1995 2005

1975 1985 1995 2005 1975 1985

1975 1985

Y

Ene

rgy

bala

nce

(kW

h/m

2 mon

th)

aaa aaa aaa aaaTRY PHPP Min Ma

Glazing type G5: g=

(a)

(b)

Fig. 5. The energy balance as the sum of heating period for two different glazings in four dfor glazing G5.

T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906 901

more than in the other orientations, which is logical and consistentwith previous reports [22]. More surprising is that also the varia-tion amplitudes of energy balance of different glazings over theheating period (DB) correlate to glazing parameters. The g-valueinfluences extraordinarily much the energy balance variance inthe southern facade whereas in the other orientations it is mainlydependent on Ug-value.

The energy balance of the glazing G1 is shown in Fig. 5a for eachdirection as the sum of the heating period months. As a comparison

1992; −0.8

2011; 3.1

1972; −3.6

2011; −0.7

Mean = −1.9

West North

Year

aaa aaa aaaax Mean St.dev. 1970−2012

0.47 and Ug=0.4 W/m2K

85; −16.0

2011; −10.5

1

1985; −18.0

2000; −13.6

Mean = −15.5

est North

1995 2005 1975 1985 1995 2005

1995 2005 1975 1985 1995 2005

ear

aaa aaa aaax Mean St.dev. 1970−2012

0.42 and Ug=1.1 W/m2K

irections. Figure (a) shows the results for glazing G1 and figure (b) shows the results

Table 4The energy balance as the sum of heating period for two different glazings in fourdirections. Mean value and standard deviation of the balance are written, alsocalculations based on standard years are shown. All units are kW h/m2 month.

South East West North

G1Mean 6.9 0.5 0.8 �1.9St.dev. 1.9 0.8 0.9 0.6PHPP 9.0 �0.4 �0.04 �4.1TRY 6.3 0.4 0.7 �2.0

G5Mean �7.7 �13.4 �13.1 �15.5St.dev. 1.7 1.1 1.2 1.1PHPP �5.4 �13.9 �13.5 �17.2TRY �7.7 �13.0 �12.7 �15.1

902 T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906

to the best glazing, the energy balance in the heating period in alldirections is in Fig. 5b shown also for the glazing G5 which has thelowest g–Ug-ratio value in the selection. It has to be noticed thatthe y-scale is different from the one in Fig. 5a but the gray regionindicating the positive balance area helps to understand and com-pare the graphs. It is clear that the highest balances for both glaz-ings are reached in year 2011. It was warm and moderately sunnyat the end of the year and was a year with most irradiation in Feb-ruary. Although, the average temperature in February was as lowas �11:2 �C. Other best-performing years were 2000 with highesttemperatures of all years in October and November and 2005 withmost irradiation in October. Worst performing years are distinc-tively different for glazings G1 and G5. First of them has the lowestenergy balance in rather warm but very cloudy 1992 whereas G5shows low balance in the very cold and also sun-scarce 1985.

Values calculated for TRY remain near the average value of43 years for both glazings, whereas values based on PHPP year dif-fer from the mean value by more than one sigma higher in thesouth and lower in north. The energy balance varies the most forthe southern windows, all other directions have similar standarddeviations we can see in Table 4.

The southern facade glazings have the best performance in thewinter, and we analyze this more thoroughly in Fig. 6 for the bothglazings shown in Fig. 5. In Fig. 6 the energy balance of the south-ern facade glazings in the heating period is shown month bymonth.

The year 2011 shows good results also monthly, and the energybalance at this year is the highest of all years in February. It is alsothe best year in November for glazing G5 but only moderate for G1.The pattern of balance between different glazings in the samemonth varies also for other cold months but copies the picture in

1974; 2.360

2005; 22.4

Mean = 10.75

2002; −4.71

1993; 4.8

Mean = −0.55

2010; −7.40

2001; −1.4

Mean = −4.19

Oct Nov Dec

−10

0

10

20

30

40

1975 1990 2005 1975 1990 2005 1975 1990 200Ene

rgy

bala

nce

(kW

h/m

2 mon

th)

aaa aaa aaa aaaTRY PHPP Min M

Glazing type G1: g=

1992; −9.2

2005; 10.3

Mean = −1.02002; −19.9

2011; −10.5

Mean = −14.9

1978; −28.0

2006; −14.2

Mean = −21.1

Oct Nov Dec

−30

−20

−10

0

10

20

1975 1990 2005 1975 1990 2005 1975 1990 200Ene

rgy

bala

nce

(kW

h/m

2 mon

th)

aaa aaa aaa aaaTRY PHPP Min M

Glazing type G5: g=

(a)

(b)

Fig. 6. The energy balance in each month of the heating period for two different glazinfigure (b) shows the results for glazing G5.

October and March. Although year 1985 was the worst over theyears, it has outstandingly low balance values only in Januaryand February.

The sum of the heating period months the energy balance val-ues calculated for TRY and PHPP year remain in the one sigmaregion for both glazings G1 and G5 as can be noted in Table 5; onlyin February the energy balance of glazing G5 based both on the TRYand PHPP year is slightly off this region.

We visualized the sign of the energy balances for all glazingsand directions during the heating period in Fig. 8, using the workby Manz and Menti [23] as an example. Fig. 7 is here to help thereader better understand how to read the subsequent two figures.One can note that each line of symbols represents one glazing typewith constant parameters, and each column represents one yearwith certain weather parameters. The diagonal line represents

9 1985; −5.571

1995; 3.5

Mean = −1.51

1974; 0.938

2011; 19.5

Mean = 6.94

1992; 11.410

1980; 39.0

Mean = 22.51

Jan Feb Mar

5 1975 1990 2005 1975 1990 2005 1975 1990 2005

Year

aaa aaa aaaax Mean St.dev. 1970−2012

0.47 and Ug=0.4 W/m2 K

1987; −31.0

1989; −13.7

Mean = −20.0

1985; −20.4

2011; −5.8

Mean = −13.0

1988; −5.2

1996; 16.6

Mean = 4.3

Jan Feb Mar

5 1975 1990 2005 1975 1990 2005 1975 1990 2005

Year

aaa aaa aaaax Mean St.dev. 1970−2012

0.42 and Ug=1.1 W/m2K

gs in the southern-oriented facade. Figure (a) shows the results for glazing G1 and

Table 5The energy balance in each month of the heating period for two different glazings inthe southern-oriented facade. The mean value and standard deviation of the balanceare indicated, and calculations based on standard years are shown. All units are kW h/m2 month.

October November December January February March

G1Mean 10.7 �0.6 �4.2 �1.5 6.9 22.5St.dev. 5.1 2.5 1.7 2.6 4.4 7.3PHPP 12.5 0.8 �3.0 1.3 10.3 25.0TRY 9.6 �0.5 �3.5 �2.9 3.8 24.4

G5Mean �1.0 �14.9 �21.1 �20.0 �13.0 4.3St.dev. 4.6 2.2 2.9 3.5 3.2 5.9PHPP 0.8 �13.6 �20.3 �16.6 �9.1 6.6TRY �2.0 �15.4 �19.8 �19.7 �15.4 6.8

Table 6Number of years with positive energy balance over the heating period for certainglazing types and orientations.

Glazing type S E W N

years % years % years % years %

G1 43 100 29 67.4 34 79.1 0 0G2 43 100 2 4.7 3 7.0 0 0G3 13 30.2 0 0 0 0 0 0G4 12 27.9 0 0 0 0 0 0G5 0 0 0 0 0 0 0 0

T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906 903

the situation where fGq ¼ f � g=Ug ¼ Gt=I; therefore, everything tothe left of this line (gray) is a net gain and to the right (white) isa net energy loss.

Orientation

All yearsnegative

All yearspositive

Half yearsboth side

G1

G2

G3G4G5

Posit

ive ba

lance

St.dev.

GainLoss

0.3

0.6

0.9

1.2

1.5

Month

0.5 1.0 1.5 2.0

Weather situation: Gt /I (m2K/W)

Gla

zing

con

figur

atio

n: G

q (m

2K

/W)

Is energy balance positive?

Fig. 7. This figure is here to help the reader understand next two graphs. Each pointin the graph shows the sign of the energy balance of one glazing (G1–G5) in oneyear (1970–2012). Points that fall into the gray area have positive energy balance(net gain), others have negative balance (net loss). Horizontal lines are theoreticalwindow quality levels with f � g=Ug ¼ Gt=I for years with the smallest and largestGt–I ratio of all years as well as the average ratio value.

0.33

0.63

0.45

0.6

0.8

South East

0.3

0.5

0.7

0.9

0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.

Gt/I

fGq

(m2 K

/W)

TRY PHPP Min(

Sign of the heating

Fig. 8. Sign analysis for the heating period as a whole. The y-axis shows the glazing qualinto the gray area are glazings with a positive energy balance in this year’s heating prespectively. (For interpretation of the references to color in this figure legend, the read

The data points in Fig. 8 are the same as in Fig. 4 but its contin-uous fGq axis allows us to find the theoretical fGq values overwhich the energy balance in all years becomes positive or belowwhich negative. Moreover, we can also more thoroughly analyzethe climatic conditions influencing the energy balance of glazingsas this is exactly what Gt=I on the x-axis shows.

We assume that technologically possible highest Gq value issmaller than 1.5 m2 K/W as our 1.18 m2 K/W was the best someyears ago. Following the standard on reduction coefficients, onecan calculate that every fGq over 1 is improbable. Therefore, itcould be possible to achieve a positive energy balance over theheating period for all the directions except north.

In eastern and western directions fGq of 0.82 m2 K/W is neededto get energy balances positive in half of the years over the heatingperiod, whereas southern direction needs only 0.45 m2 K/W.Moreover, 0.63 m2 K/W is enough to achieve a positive energy bal-ance for south facing glazings in all years.

In Fig. 8 the results of the heating period summary energy bal-ance sign analysis are shown. We observe that it is almost impos-sible to get all years to be in the positive energy balance in anydirection other than south. However, half of the years can exhibita positive energy balance for eastern and western facades if theGq value is higher than 1.13 m2 K/W, which the glazing G1 exceeds.In the north-oriented facade, it is not possible to achieve even oneyear over the zero balance line, whereas in the southern directionall years have positive energy balances already if Gq value exceeds0.87 m2 K/W. The number of years which fall into the net gain areaare counted in Table 6. We note the exact numbers indicating thatonly the glazing G1 has positive energy balance in the eastern andwestern facades in more than half of the years: in 29 and 34 yearsrespectively. Although the glazing G2 has also 100% of the yearspositive for the southern-oriented windows, there are no signifi-cant numbers of years with positive energy balance of this glazingtowards east and west. Glazings G3 and G4 exhibit in approxi-mately 30% of the years a positive energy balance for the southern

3

2

0.60

0.78

0.95West North

0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0

(m2K/W)

Best) Max(Worst) Mean

period energy balance

ity and the x-axis the climatic situation in a certain month and year. Points that falleriod. Green and purple dots show the climate situation in PHPP and TRY year,

er is referred to the web version of this article.)

0

10

20

30

40

Yea

rs

South East West North TRY PHPP Min Max Mean

Number of years with positive energy balance over heating period at theoretical fGq value

0.30

0.60

1.40

1.80

0.38

0.91

0.86

1.98

0.45

0.78

1.18

0.63

0.99

0.96

1.62

0.33

0.82

fGq (m2 K/W)

Fig. 9. The number of the years out of 43 in which a positive energy balance of a glazing with any fGq value is reached. Each line represents all glazings in one cardinaldirection, and dashed lines show the 5 glazing types used for calculations in this paper. Different dots on the lines show when a positive balance is reached for the PHPP andTRY year, as well as years with minimum, maximum and mean Gt=I value.

904 T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906

windows, but glazings in all other directions show a negative bal-ance. Moreover, the glazing G5 does not have positive energy bal-ance in a single year in any direction.

To observe the sign change more theoretically we look for theglazing fGq values over which the energy balance of a glazingbecomes positive each year. Fig. 9 shows the steps for each yeargetting positive as fGq increases.

We observe that the line for the southern facade is more steepthan for the other directions, it takes a change ofDfGq ¼ 0:3 m2 K/W to increase the number of years with positiveenergy balance from 0 to 43 in the southern facade butDfGq ¼ 0:66 m2 K/W in the northern facade. To get the energybalance of the southern windows positive, the change ofDfGq ¼ 0:3 m2 K/W in window quality has to be made from thecurrent Estonian standard.

It is more difficult to reach a positive balance in the east andwest, and it is impossible to get all years positive by means ofthe current technology. This applies also for any glazing in thenorthern direction, in which the energy balance of any glazing atany year remains negative. Moreover, the very extreme PHPP yearindicates it is possible that the situation of the northern facade iseven worse than we have calculated, or is itself under-estimatingthe Estonian climate.

It has been found before that in cold climates, it is almostimpossible to achieve a positive energy balance of glazings facingany other direction than south [23]. The coldest location chosenby these authors was Moscow, where the energy balance of thebest glazing type (which is also used in this study as G1) achieveda positive energy balance in December. In our case the same glaz-ing would also achieve a positive energy balance in the standardyears if the reduction factor for g–Ug ratio is not used. However,it does not achieve a positive energy balance in a year that has amean ratio of climatic parameters (Gt=I). This means there aremany years when the energy balance is negative, although the cal-culation based on standard years shows positive balance.

4. Conclusions

In this paper, the authors sought to provide a clear practicalexample architects could use to optimize the design of buildingenvelope geometry and transparent areas at the very early design

phase, as well as in later design phases the selection of windowglazings appropriate for local conditions. This article examinedlong-term patterns in energy performance of windows, aiming toclarify whether it is possible to achieve positive energy balancesfor various window glazing in the Nordic climate of Estonia.Measured climate data from years 1970 to 2012 were used forthe analysis of glazings in four cardinal directions and on horizon-tal plane. Calculations based on measured data were compared tothe ones done for standard energy calculation years.

There are five main conclusions that can be drawn from thiswork:

1. All 43 years could experience positive energy balance of glaz-ings if the f � g=Ug was over– 0.63 m2 K/W in the southern facade,– 0.99 m2 K/W in the eastern facade,– 0.96 m2 K/W in the western facade,– 1.62 m2 K/W in the northern facade.Selected glazings G1 and G2 exceeded the limit for the southernorientation, G1 achieved positive balance over the heating per-iod in about 70% and 80% of the years in eastern and westernfacades respectively. Energy balance of the selected glazingsremains always negative over the heating period in the north-ern direction.

2. In addition to October and March, the positive energy balance inall directions is possible to reach also in February. In December,the energy balance values of all selected glazings in all direc-tions are always negative; this holds mostly for Novemberand January too.

3. The year-to-year differences of energy balance depend both onclimatic parameters and glazing type. Amplitude of the fluctua-tions over the heating period correlates to g-value in southernand Ug-value in other directions.

4. In January and February, energy balance of glazings (withhigher Ug-values) depends mostly on temperature while inother months (for lower Ug-values) years with high level of irra-diation experience also higher energy balances. In Novemberglazing G1 had the highest energy balance in the brightest butat the same time the coldest year. In all directions, the year2011 was one of the highest peaks of energy balance over theheating period.

Table A.7Factors to convert irradiation data from a horizontal surface to vertical surfaces [32].

Month South East West North

January 6.15 0.91 0.96 0February 3.15 0.7 0.96 0March 1.76 0.66 0.77 0April 0.98 0.61 0.54 0.02May 0.6 0.57 0.48 0.09June 0.48 0.54 0.47 0.13July 0.52 0.54 0.48 0.11

T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906 905

5. Both climatic parameters and calculated energy balances ofstandard year TRY fit into a standard deviation region of mea-sured years in the heating period. Although it applies also formeasured climatic parameters of PHPP, it is lower comparedto the irradiation converted on northern walls. Energy balancevalues based on PHPP are exceeding the one sigma region insouth facade and fall behind in north. The main differencescome from the period of January to March.

In this article, only the glazing portion of the transparent part ofthe window was analyzed during the heating period. Future effortsmay build on the present work. It would be needed in practice todo the calculations also for the entire windows, while varyingthe frame types and fragmentation percentage. Also the coolingseason problems could be assessed and the influence of the win-dows to the whole building evaluated during long period observed.To further expand on the proof-of-concept results presented here,another approach could be a dynamic simulation of the entirebuilding.

August 0.79 0.57 0.54 0.05September 1.34 0.66 0.6 0October 2.42 0.7 0.7 0November 4.89 0.93 0.93 0December 7.29 0.91 0.94 0

Acknowledgments

This research was supported by the European Union throughthe European Regional Development Fund, facilitated by the

South East

−201040

−201040

−201040

−201040

−201040

−201040

−201040

−201040

−201040

−201040

−201040

−201040

1975 1990 2005 1975 1990 2005

Y

Gla

zing

ene

rgy

bala

nce

per m

onth

(kW

h/m

2 )

Glazing energy balance for different glaz

Fig. A.10. All calculation results in one figure. Each small graph shows the energy balancyears 1970–2012. In the months with tinted gray background, the energy balance of th

Archimedes Foundation. Authors give their special thanks toJaanus Hallik, Targo Kalamees, Erko Jakobson, Kalju Eerme andValeria Siirand for the help in acquiring and/or transforming theclimate data used in this paper.

Appendix A

See Table A.7 and Fig. A.10.

West North

JanFeb

Mar

Apr

May

JunJul

AugS

eptO

ctN

ovD

ec

1975 1990 2005 1975 1990 2005

ear

ing types: G1 G2 G3 G4 G5

e of 5 different glazings oriented to one cardinal direction in one month during thee 4 better glazings is always positive.

906 T.M. Kull et al. / Energy Conversion and Management 89 (2015) 896–906

References

[1] EN 410:1998 Glass in building – determination of luminous and solarcharacteristics of glazing; 1998.

[2] EN 673:1998 Glass in building – determination of thermal transmittance (Uvalue) calculation method; 1998.

[3] PHPP passive house planning package; 2013.[4] EVS-EN ISO 13790:2008 Energy performance of buildings – calculation of

energy use for space heating and cooling; 2008. p. 1–171. <https://wiki.umn.edu/pub/PA5721_Building_Policy/WebHome/LEEDENERGYSTAR_STUDY.pdf>.

[5] Directive 2010/31/EU of the European Parliament and of the Council of 19 May2010 on the energy performance of buildings (2010) 1–23.

[6] Feist W, Schnieders J, Dorer V, Haas A. Re-inventing air heating: convenientand comfortable within the frame of the passive house concept. Energy Build2005;37(11):1186–203. http://dx.doi.org/10.1016/j.enbuild.2005.06.020.

[7] Mauring T, Reinberg GW, Hallik J, Valge M, Kalbe K. A prototype architecturefor passive and plus energy building in Estonia. In: 6th passive houseconference in the Nordic countries; 2013.

[8] Lam JC, Wan KK, Tsang C, Yang L. Building energy efficiency in differentclimates. Energy Convers Manage 2008;49(8):2354–66. http://dx.doi.org/10.1016/j.enconman.2008.01.013. <http://linkinghub.elsevier.com/retrieve/pii/S0196890408000393>.

[9] Kull TM. Päikeseenergia passiivse kasutamise potentsiaal erineva geomeetriaja komponentidega hoonetel Eesti kliimas (Passive solar heating potential ofdifferent building designs in Estonian climate); 2013. <http://hdl.handle.net/10062/30967>.

[10] Peippo K, Lund P, Vartiainen E. Multivariate optimization of design trade-offsfor solar low energy buildings. Energy Build 1999;29(2):189–205. http://dx.doi.org/10.1016/S0378-7788(98)00055-3.

[11] Haese G. Analysis of the influences of solar radiation and façade glazing areason the thermal performance of multi-family buildings. PhD thesis, TechnicalUniversity of Bialystok; 2010.

[12] Doulos L, Tsangrassoulis A, Topalis F. Multi-criteria decision analysis to selectthe optimum position and proper field of view of a photosensor. EnergyConvers Manage 2014;86:1069–77. http://dx.doi.org/10.1016/j.enconman.2014.06.032. <http://www.sciencedirect.com/science/article/pii/S0196890414005573>.

[13] Gasparella A, Pernigotto G, Cappelletti F, Romagnoni P, Baggio P. Analysis andmodelling of window and glazing systems energy performance for a wellinsulated residential building. Energy Build 2011;43(4):1030–7. http://dx.doi.org/10.1016/j.enbuild.2010.12.032.

[14] Bokel RMJ. The effect of window position and window size on the energydemand for heating, cooling and electric lighting. In: Building simulation;2007. p. 117–21.

[15] Hassouneh K, Alshboul A, Al-Salaymeh A. Influence of windows on the energybalance of apartment buildings in Amman. In: Energy conversion andmanagement, Univ Jordan, Fac Engn {&} Technol, Architecture Engn Dept,Amman 11942, Jordan; 2010. p. 1583–91. doi: http://dx.doi.org/10.1016/j.enconman.2009.08.037.

[16] Leskovar VZ, Premrov M. Influence of glazing size on energy efficiency oftimber-frame buildings. Constr Build Mater 2012;30:92–9. http://dx.doi.org/10.1016/j.conbuildmat.2011.11.020.

[17] Poirazis H, Blomsterberg Ak, Wall M. Energy simulations for glazed officebuildings in Sweden. Energy Build 2008;40(7):1161–70. http://dx.doi.org/10.1016/j.enbuild.2007.10.011.

[18] Ghisi E, Tinker JA. An Ideal Window Area concept for energy efficientintegration of daylight and artificial light in buildings. Build Environ2005;40(1):51–61. http://dx.doi.org/10.1016/j.buildenv.2004.04.004.

[19] Inanici MN, Demirbilek FN. Thermal performance optimization of buildingaspect ratio and south window size in five cities having different climaticcharacteristics of Turkey. Build Environ 2000;35:41–52. http://dx.doi.org/10.1016/S0360-1323(99)00002-5.

[20] Persson M-L, Roos A, Wall M. Influence of window size on the energy balanceof low energy houses. Energy Build 2006;38(3):181–8. http://dx.doi.org/10.1016/j.enbuild.2005.05.006.

[21] Bülow-Hübe H. The effect of glazing type and size on annual heating andcooling demand for Swedish offices. In: Proceedings of renewable energytechnologies in cold climates; 1998. <http://www.belok.se/docs/dagsljus/3_varme_och_kylbehov.pdf>.

[22] Karlsson J. Windows: optical performance and energy efficiency; 2001. p. 49.<http://uu.diva-portal.org/smash/record.jsf?pid=diva2:161001>.

[23] Manz H, Menti U-P. Energy performance of glazings in European climates.Renew Energy 2012;37(1):226–32. http://dx.doi.org/10.1016/j.renene.2011.06.016.

[24] Hong T, Chang W-K, Lin H-W. A fresh look at weather impact on peakelectricity demand and energy use of buildings using 30-year actual weatherdata. Appl Energy 2013;111:333–50.

[25] Pernigotto G, Prada A, Cóstola D, Gasparella A, Hensen JL. Multi-year andreference year weather data for building energy labelling in north Italyclimates. Energy Build 2014;72:62–72. http://dx.doi.org/10.1016/j.enbuild.2013.12.012. <http://linkinghub.elsevier.com/retrieve/pii/S037877881300827X>.

[26] Yang L, Lam JC, Liu J, Tsang CL. Building energy simulation using multi-yearsand typical meteorological years in different climates. Energy Convers Manage2008;49(1):113–24. http://dx.doi.org/10.1016/j.enconman.2007.05.004.

[27] Kapsomenakis J, Kolokotsa D, Nikolaou T, Santamouris M, Zerefos S. Fortyyears increase of the air ambient temperature in Greece: the impact onbuildings. Energy Convers Manage 2013;74:353–65. http://dx.doi.org/10.1016/j.enconman.2013.05.005. <http://www.sciencedirect.com/science/article/pii/S0196890413002525>.

[28] Kalamees T, Kurnitski J. Estonian test reference year for energy calculations.Proc Estonian Acad Sci, Eng 2006;12(1):40–58. <http://books.google.com/books?hl=en&lr=&id=wWK2EBgSriEC&oi=fnd&pg=PA40&dq=Estonian+test+reference+year+for+energy+calculations&ots=A84_OP4hCC&sig=pTDa6qfNPGXFXBZ92xYpRpLuLY8>.

[29] Meteonorm Software; 2012.[30] Passivhaus Institut [online].[31] EMHI. Eesti Meteoroloogia ja Hüdroloogia Instituut [online].[32] Russak V, Kallis A. Eesti kiirguskliima teatmik (Handbook of Estonian solar

radiation climate); 2003. <http://scholar.google.com/scholar?q=related:00ptDt64mSEJ:scholar.google.com/&hl=en&num=20&as_sdt=0,5>.

[33] Hastings S. Breaking the heating barrier: learning from the first houseswithout conventional heating. Energy Build 2004;36(4):373–80. http://dx.doi.org/10.1016/j.enbuild.2004.01.027.

[34] Lane DM. Measures of variability. <http://onlinestatbook.com/2/summarizing_distributions/variability.html>.

[35] Notton G, Poggi P, Cristofari C. Predicting hourly solar irradiations on inclinedsurfaces based on the horizontal measurements: performances of theassociation of well-known mathematical models. Energy Convers Manage2006;47(13–14):1816–29. http://dx.doi.org/10.1016/j.enconman.2005.10.009.<http://linkinghub.elsevier.com/retrieve/pii/S0196890405002645>.

[36] Muzathik AM, Ibrahim MZ, Samo KB, Wan Nik WB. Estimation of global solarirradiation on horizontal and inclined surfaces based on the horizontalmeasurements. Energy 2011;36(2):812–8. http://dx.doi.org/10.1016/j.energy.2010.12.035.

[37] Huang W, Xu Q. Development of an analytical method and its quick algorithmto calculate the solar energy collected by a heliostat field in a year. EnergyConvers Manage 2014;83:110–8. http://dx.doi.org/10.1016/j.enconman.2014.03.065. <http://www.sciencedirect.com/science/article/pii/S0196890414002660>.

[38] Oteiza P, Pérez-Burgos A. Diffuse illuminance availability on horizontal andvertical surfaces at Madrid, Spain. Energy Convers Manage 2012;64:313–9.http://dx.doi.org/10.1016/j.enconman.2012.05.022. <http://www.sciencedirect.com/science/article/pii/S0196890412002427>.

[39] Stanciu C, Stanciu D. Optimum tilt angle for flat plate collectors all over theWorld – a declination dependence formula and comparisons of three solarradiation models. Energy Convers Manage 2014;81:133–43. http://dx.doi.org/10.1016/j.enconman.2014.02.016. <http://www.sciencedirect.com/science/article/pii/S0196890414001265>.

[40] Tsikaloudaki K, Theodosiou T, Laskos K, Bikas D. Assessing cooling energyperformance of windows for residential buildings in the Mediterranean zone.Energy Convers Manage 2012;64:335–43. http://dx.doi.org/10.1016/j.enconman.2012.04.020. <http://www.sciencedirect.com/science/article/pii/S0196890412002075>.

[41] Ebrahimpour A, Maerefat M. Application of advanced glazing and overhangs inresidential buildings. Energy Convers Manage 2011;52(1):212–9. http://dx.doi.org/10.1016/j.enconman.2010.06.061. <http://www.sciencedirect.com/science/article/pii/S0196890410002827>.

[42] PHI, Component Award 2014, Tech rep; 2014.[43] Yas�ar Y, Kalfa SM. The effects of window alternatives on energy efficiency and

building economy in high-rise residential buildings in moderate to humidclimates. Energy Convers Manage 2012;64:170–81. http://dx.doi.org/10.1016/j.enconman.2012.05.023. <http://www.sciencedirect.com/science/article/pii/S0196890412002439>.

[44] Mauring T, Hallik J, Valge M, Kalbe K. A prototype architecture for passive andplus energy building in Estonia. In: Proceedings of 6th passive houseconference in the Nordic countries; 2013.