Radiação Residencial - 2012

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Modeling and simulation of the energy use in an occupied residential building

in cold climate

Thomas Olofsson a,, T.M.I. Mahlia b,c

a Department of Applied Physics and Electronics, Ume University, SE-901 87 Ume, Swedenb Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiac Department of Mechanical Engineering, Syiah Kuala University, Banda Aceh 23111, Indonesia

a r t i c l e i n f o

Article history:

Received 9 February 2011Received in revised form 2 August 2011Accepted 3 October 2011Available online 9 November 2011

Keywords:

Low energy buildingTEKLA simulationEnergy use in buildingEnergy savingsEnergy and building

a b s t r a c t

In order to reduce the energy use in the building sector there is a demand for tools that can identify sig-nificant building energy performance parameters. In the work introduced in this paper presents a meth-odology, based on a simulation module and graphical figures, for interactive investigations of the buildingenergy performance. The building energyuse simulation program is called TEKLA and is using EN832 withan improved procedure in calculating the heat loss through the floor and the solar heat gain. Thegraphicalfigures are simple and are illustrating the savings based on retrofit measures and climate conditions.

The accuracy of theTEKLAsimulation wasinvestigated on a typical single-family building in Swedenfora periodof time in a space heating demandof relatively cold andmild climate. Themodel wasfoundappli-cable forrelative investigations. Further, themethodologywas applied on a typical singlefamily referencebuilding. The climate data from three locations in Sweden were collected and a set of relevant measureswere studied. The investigatedexamples illustrate howdecisions in the early stages of the building designprocess can have decisive importance on the final building energy performance.

2011 Elsevier Ltd. All rights reserved.

1. Introduction

There is no guarantee that a low energy building is energy effi-cient. The buildings can be low energy just because of the occupa-tion schedules, low amenities, etc. The same contradictions can befound for energy efficient buildings not being low energy. The effi-cient equipment may not be appropriate installed to reduce themost adequate energy using loads. The more appraisals are high-energy buildings with good efficiency that would be very energyconsuming without the efficient equipment. In order to be efficientthe building must contain elements from three categories[1]:

i. Must supply the amenities and features appropriate for thatkind of building.

ii. Must be a low energy building, which means that the build-ing must be operated in such a manner as to be efficient.

iii. Must be energy efficient which means that the buildingmust contain energy-efficient technologies and design that,when operating as designed, will effectively reduce energyuse.

Different techniques, or models, are used in order to investigatethe building energy efficiency in the design stage for new as well asfor retrofitted buildings. Models for energy estimations can be cat-egorized into two broad but distinct categories, data-driven mod-els and forward models[2].

Data driven models are based on known input and output. Themodels are more reliable for finding building performance condi-tions from a monitored period. In the literature a wide range ofmodels has been investigated for energy estimations. Examplesof data driven-models are for example simple linear regressions[3], Kalman filters [4], ARMA [5], Artificial Neural Networks(ANN) [6] and Multivariate Statistical (MVS) [7], ANNOVA [8],Monte-Carlo[9].

The forward model will predict the output from a specific modelwith known structure and input parameters of a building. From theperspective of buildingenergy investigations, a benefit is the build-ing need not to be physical built. The forward model is ideal in thepreliminary design and analysis stage. Examples of forward modelsare: degree-hour or -day methods[10],thermal networks[11]re-sponse factors[12], finite-difference method[13].

This study presents an example of a developed building energysimulation program referred to as a forward model. It is called TEK-LA and can be used for estimating energy use for residential build-ings[14,15]. The model is based on the European standard, EN832[16] with two additional procedures to consider the heat lossthrough the floor and the solar gain.

0306-2619/$ - see front matter 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.apenergy.2011.10.002

Corresponding author at: Department of Applied Physics and Electronics, UmeUniversity, SE-901 87 Ume, Sweden. Tel.: +46 90 786 6710.

E-mail address: [email protected](T. Olofsson).

Applied Energy 91 (2012) 432438

Contents lists available at SciVerse ScienceDirect

Applied Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a p e n e r g y
http://dx.doi.org/10.1016/j.apenergy.2011.10.002mailto:[email protected]://dx.doi.org/10.1016/j.apenergy.2011.10.002http://www.sciencedirect.com/science/journal/03062619http://www.elsevier.com/locate/apenergyhttp://www.elsevier.com/locate/apenergyhttp://www.sciencedirect.com/science/journal/03062619http://dx.doi.org/10.1016/j.apenergy.2011.10.002mailto:[email protected]://dx.doi.org/10.1016/j.apenergy.2011.10.002


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EN832 is a steady-state model, but it takes in account internaland solar gain through a utilization factor. It means that the modeldoes not consider the thermal inertia of the building when the heatloss is calculated and the potential savings of space heating andcooling by the dynamic effect.

Several papers report on simulation models based on EN 832. Ina paper by Kalema [17] a model calledOPTIX is introduced for opti-mizing the energy economy of buildings. The calculation of thesupplied energy use for space heating and cooling is based onthe EN 832 standard. In that study the model was found to corre-spond well with monitored data from some occupied residentialbuildings. The annual energy use of a typical single-family housecalculated with EN 832 has also been compared with the resultscalculated with a dynamic thermal analysis program, EPIQR, andreliable results were obtained [18]. The authors concluded thatthe overall energy requirement for space heating based on EN832 seems to be sufficiently accurate. The scope of the work pre-sented in this article is to verify and use TEKLA to estimatevariations of significant parameters influencing the energy use ofthe building in design of new and retrofitted energy-efficientbuildings.

In the literature one find other examples of parameter investi-gations for improving building energy performance. Examplescan be found fromAustralian conditions [19], Serbian [20] and Sau-di Arabia[21]where Energy Plus has been used for the simulation.Another similar study based on Chinese conditions was conductedwith eQuest [22]. But, also experimental studies has been per-formed, e.g. for the Kingdom of Bahrain[23]. A larger overviewof methods for benchmarking building energy performance canbe found in[24].

2. Data collections

Data from a monitored occupied residential building was used

to evaluate the results from the TEKLA-model. The monitoredbuilding will be referred to as reference building. It is a singlestory single-family building located in Ume, Sweden. The refer-ence building was built in 1974 and is heated by electricity. Thedomestic hot water was also supplied by electricity and an exhaustfan ventilated the building (there was no heat recovery of the ex-changed air). The windows were double glazed with a coupleddouble window frame.

The evaluation of TEKLA was made on monitored data for twomonths. The thermal performance data of the reference buildingare presented in a previous report written by Jonsson and stin[25]. The transmission data of the reference building is listed inTable 1.

The total ventilation exhaust airflow for the monitored single

family reference building was 49 l/s [26]. This airflow was takenas the total airflow which should be heated. The metabolic gainsfrom occupants in the reference building were stated as a dailyaverage of 50 W/m2 body area. The distribution of the windowareas towards different directions and the active window area is gi-ven inTable 2. The solar radiation is based on measured data bySMHI[27]. The monthly average daily global solar radiations for

selected moths are 0.83 kW h/day and 2.23 kW h/day where shad-ing factor value of 0.4 for February and 0.7 for March respectively.

The TEKLA-simulations are based on a building simple designreferred as base-case and is collected from the blue-prints of thereference building. The characteristics of the base-case buildingare presented inTable 3.

The characteristics for the base-case building is used in simula-tions of climate data for three different locations in Sweden,namely Kiruna, Ume and Lund. Climate data and latitudes forthe locations are given inTable 4.

The annual energy use is the sum over all months during theyear for which the average external temperature is lower than

11

C. For the base-case building the total energy use were7.6 MW h for Ume, 10.8 MW h for Kiruna and 3.5 MW h for Lund.

3. Methodology

The TEKLA model is implemented for simulations of energy use,using the MATLAB software. The simulation program is developedto predict existing buildings in cold and mild climate. It is adjustedto a single-thermal zone building that has a uniform internal tem-perature and the energy use is calculated monthly, according toEN832. A more detailed explanation of the application of EN 832into the model of TEKLA is given by Sjsten et al.[14]and Bomanet al.[15].

To improve EN832 two additional calculation procedures, the

heat loss through the floor and solar heat gain, has been used.The heat loss through the floor is calculated using the procedure gi-ven in EN ISO 13 370[28]. In this paragraph the theory behind thecalculations is introduced. The heat loss is treated first, then, thesolar gain is treated separated because of the absence of recom-mendations to determine the insolation in EN 832. The solar gainconsists of the solar radiation through the windows.

3.1. Heat loss

In this paragraph the theory behind the heat loss calculation isdiscussed.

3.1.1. Total heat loss

The implemented model is based on thermal properties ofbuilding, average values of a studied time period of the buildinginterior and exterior temperature, ventilation airflow, and heat lossthrough the floor/slab, _Qfloor. The total heat loss, Ql, is calculatedaccording to:

Ql Hhi he _Qfloort 1

whereH isthe heat loss coefficient of the building (except from thefloor), h i, the average interior temperature, he, the average exteriortemperature, and t the duration of the calculation period,respectively.

3.1.2. Heat loss coefficient

The heat loss coefficient of the building (except from the floor)can be calculated by the following equation:

Table 1

Transmission data of the reference building.

Component Area (m2) U-value (W/m2 K)

External wall 109.2 0.37Window 18.8 2.60External door 3.0 2.00

Roof 137.0 0.10

Table 2

Window data of the reference building.

Location Part of totalwindow area

Active windowarea (m2)

North-west 0.40 5.25South-west 0.32 4.21North-east 0.28 3.59

T. Olofsson, T.M.I. Mahlia/ Applied Energy 91 (2012) 432438 433


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HXUiAi qaCa

_V1 gven:rec 2

where Ui, is the thermal transmittance (U-value) of the buildingcomponent, A i, the area of the building component, _V, the ventila-tion airflow,qa, the density of air, Ca, the heat capacity of air andgven.rec the efficiency of ventilation heat recovery device,respectively.

3.1.3. Total internal heat gain

The total internal heat gains, Qg, has been determined as thesum of the measured energy use for appliances and lighting, Qapp,20%, according to Olofsson [29], the measured energy use fordomestic hot water preparation,Qhw, and the metabolic heat gains,Qm, are based on daily average dissipation of heat from occupants.The solar heat gain for the used calculation period,Qs, is calculatedusing the commercial energy simulation software ENORM 1000.The total heat gain, Qg, can thus be written as:

Qg Qm 0:2Qhw Qapp Qs 3

3.1.4. Total heat loss

The calculated total heat loss and total heat gain are then usedto obtain the space heating use for the building, Qh,for the calcu-lation period can be calculated by the following equation:

Qh Ql gutiQg 4

where Qlis the total heat loss of the building and the utilization fac-tor, guti, which is an efficiency factor used to measure how much theheat gain that can be utilized to reduce the supplied energy forspace heating.

The usedguti depends on the ratio between the total heat gainand the total heat loss of the building, the time constant of thebuilding. If the calculation period is for a month or the whole heat-ing season, the calculation is by the following equation:

guti 1 ca

1 ca1 ifc 1 5

guti a

a 1 ifc 1 6

where

c QgQl

7

a a0 ss0 8

wheres is the time constant of the building.The numerical values of the reference time constant, s0 is 16

anda0is 1 for monthly calculation periods and 28 and 0.8, respec-tively, for seasonal heating periods.

3.2. Solar heat gain

In this section the theory behind the solar heat gain simulationsis discussed. The solar energy on a surface on earth is considered toconsist of three different parts. The direct part of solar radiationthat reaches the earths surface without being scattered or ab-sorbed by the atmosphere is called direct solar radiation, Hdir.

The scattered radiation is assumed to reach the earths surfacefrom all directions and is called diffuse solar radiation, Hdiff. Thediffuse radiation varies from 10% of the total radiation on a clearday to nearly 100% on a totally cloudy day. Additional to the directand diffuse part there is a reflected part from the ground and sur-rounding, ground-reflected solar radiation,Hgr. The direct part andthe diffuse part compose the global radiation,H, which is definedas:

H Hdir Hdiff 9

3.2.1. Solar radiation through windows

The spectral transmissivity of glass is such that it is transparentto short-wave radiation (over 90% of the radiation falls in the vis-

ible range) emitted by the sun and reflected by the sky but nearlyopaque to long wave thermal radiation (roughlyk= 3 lm) emittedby the interior of the room. The solar gain through a window de-pends on the thermal transmission (the U-value), the total solarenergy transmittance (theg-value) and the area collecting the radi-ation. Original double pane windows have a thermal transmissionof the glazed part of 2.8 W/m2 K. The thermal transmission of glaz-ing systems has been reduced by applying a low emission coatingto one of the glass panes facing the cavity it also reduce the energytransmitted by radiation. Using low emission coating and gas fill-ings a thermal transmission of 1.1 W/m2 K is obtained for a stan-dard energy glazing with two 4 mm glass panes and 15 mm gap.The total solar energy transmittance for a traditional double paneglazing system is approximately 76% and decreases as the glazing

is applied with a low emissions coating to a value in the range of7259% depending on the type of coating [30]. The monthly solar

Table 3

Characteristics of the base-case building for simulations.

Residential building base-case

Location Umea 63.8TN (Sweden)Floor Suspended floorDimension (envelope) 13.0 7.8 2.4 m3

Window area 14 m2

Heating set-point 201C

Ventilation (incl. infiltration) 0.5 h1Internal heat gain, Qint 5.78 MW h/yearComponents U-value (W/m2)

External wall 0.21Window 1.20External door 0.81Floor 0.19Roof 0.17

Table 4

Climate data and latitudes for Kiruna, Umea and Lund.

Location Latitude (N) Annual averagetemperature (C)

Yearly global solarradiation (kW h/m2)

Kiruna 68 1.2 816Umea 64 2.7 938Lund 53 7.9 973

434 T. Olofsson, T.M.I. Mahlia/ Applied Energy 91 (2012) 432438


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gain,Qsun, through the glazing of the window into the building canbe calculated by the following equation:

Qsun gdirHdir;tgdiffHdiff;t Hgr;tAactiveF 10

wheregdirandgdiffare transmittance of the direct radiation and thediffuse-and ground-reflected radiation, Hdir;t, Hdiff;t and Hgr;t aremonthly average radiation on a tilted surface divided into direct,

diffuse and ground-reflected parts, Aactiveis the area collecting theradiation, F is a shading factor. A simple model that can be usedto adjust the total solar radiation transmittance,gdir, due to the inci-dence angle of the direct solar radiation is given by Karlsson andRoos[31]:

gdir g?1 tanxi=2 11

whereg?is transmittance at normal incident and can be calculatedaccording to EN 410[32,33], x is a variable exponent, i is the inci-dent angle of the direct solar radiation. The model fits well at lowangles of incidence but is limited when it comes to different typesof double or triple glazed units and coated glazing, and the valueofx equals about 4 for most windows [31]. The total diffuse solarradiation transmittance is defined as[34]:

gdiff f g? 12

where fis a correction factor. For a double-glazing with one low-emissive coating a variable exponent ofx= 3 and a correction factorfor the diffuse solar radiation off= 0.86 is found representative [34].

3.2.2. Monthly average daily radiation on a tilted surface

For solar contribution to annual energy use of buildings, longtime scales are relevant. When the energy use over the lifetimeof a building is studied, the solar radiation data should be repre-sented of the average of many years [35]. The most commonlyavailable solar radiation data are the total radiation on a horizontalsurface, whereas the need often is for radiation on a tilted surface.The general problem is to calculate the radiation on a tilted surface

when only the total radiation on a horizontal surface is known. Amethod to estimate the monthly average daily total radiation inci-dent on a tilted surface oriented directly towards the equator hasbeen developed by Liu and Jordan[36]. They assumed the diffuseand ground-reflected radiation to be isotropic. They also suggestedthat the monthly average direct radiation tilt factor, RB, could beestimated by assuming that it has the value which would be ob-tained if there were no atmosphere, in general it is a function oftransmittance of the atmosphere. Later the method to calculatethe monthly average daily radiation on a tilted surface for surfacesfacing east or west of south has been developed by Klein[37]. Themonthly average daily radiation on a tilted surface, HT, is definedas:

HT H 1

Hdiff

H !

RB Hdiff

1 cos s

2

Hqg1 cos s

2

13

where H is the monthly average daily total radiation on a terrestrialhorizontal surface,Hdiff=His the monthly average diffuse-to-totalradiation ratio for a horizontal surface, s is the tilt of the surfacefrom horizontal; qg is the ground reflectivity. The diffuse-to-totalradiation ratio for a horizontal surface is determined by Hsieh[33]. Rewriting Eq.(5)to an overall tilt factor gives:

R HT

H 1 Hdiff

H

!RB Hdiff

H

1 cos s

2

qg

1 cos s

2

14

and the monthly average daily radiation on a tilted surface is thencalculated by the following equation:

HT R H 15

The method to consider the solar gain used in TEKLA is based onKlein[38]and a further development of the method given by Liuand Jordan[36]. It differs from the earlier method in the approachof how the monthly average direct radiation is determined. It is abit more cumbersome to use but shows improved results on theisotropic method when compared with integrated hourly calcula-tions for many years of radiation data. The method is for surfacesof any orientation in general. If

wis not 0 (or 180), the sunrise

and sunset on the tilted surface will not be symmetrical about solarnoon. This method of calculating the tilt factor works for all surfaceorientations and all latitudes (including negative latitudes, for thesouthern hemisphere). It is valid whether the sun rises or sets onthe surface twice each day (e.g., on north-facing surfaces when dis positive) or not at all[39].

4. Results and discussion

The result of an evaluation of the accuracy of the TEKLA simu-lation model is discussed below. It is used for illustrating variationin energy use for space heating based on a set of refurbishmentmeasures and gained solar heat is introduced. The presentation is

based on graphical figures.

4.1. An evaluation of the accuracy of the TEKLA model

Some of the results of a TEKLA simulation of the base-casebuilding using climate data from Ume are presented inTable 5.

The simulated base-case building is a typical occupied single-family building in Ume, Sweden. Measurements on that particularhouse were conducted in the months February and March. That is arepresentative period with both cold and mild climate. Collecteddata was used for the evaluation. The simulation for February gavean energy use of 3.23 MW h for space heating. Compared to themeasured data that is an overestimation by 4%. The simulationfor March gave a value of 2.29 MWh for space heating. When eval-

uated on monitored data, that is an underestimation of 0.5%. Theevaluation of the accuracy in this study the absolute accuracy ofthe model is limited. Additionally, the thermal transmittance val-ues may include some errors. For a detailed evaluation of the mod-el there are needs for more measured data of the total energy useon a larger set of buildings. Nevertheless, it is indicated that theTEKLA model is applicable and in acceptable agreement with theempirical data for a typical residential building in Sweden on a per-iod with cold and mild climate. Thus, the TEKLA model was foundto be useful as a tool to compare the relative variation in energyuse based on different possible refurbishment measures.

4.2. An investigation of building refurbishment measures

The TEKLA model has been used to investigate a set of refur-bishment measures for the base case building. In this investigationthe default distribution of the total window area are 53% at southand 47% at north. The results are shown in the ratio of the calcu-lated energy use of the base-case building as a function of the fol-lowing parameters of refurbishment measures, roof insulationthickness, wall insulation thickness, window U-value and ventila-tion heat recovery.

In order to reduce the energy use for heating a frequent usedrefurbishment in Sweden is to add additional roof insulation tothe building. The influence of insulation on the energy use dependsmainly on the roof area and the insulation thickness. Due to thecold climate in Sweden most single-family buildings has a thickinsulation cover of about 0.25 m. The influence of roof insulation

on the energy use will be less if more insulation is added.Fig. 1illustrates the modeled influence of additional insulation at the

T. Olofsson, T.M.I. Mahlia/ Applied Energy 91 (2012) 432438 435


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roof and the wall respectively. As shown in this figure the insula-tion thickness for the investigated climate has an obvious influencethe first 0.20 m. This result is interesting froman economic point ofview to find the optimal operating thickness of the insulation. Animproved in U-value of the wall caused by additional insulationdoes not have to be of high magnitude to have an impact on the en-ergy use. The reason is the large area of the wall comparing to theother building components. For example addition of 0.14 m wellinsulation reduces the annual energy use by 1216%, dependingon location of the base-case building. The same refurbishment ofthe roof reduces the annual energy use by 79%.

The windows represent a small part of the total area of theenvelope, usually about 20%. But, because of high U-value of win-dows the energy use will decrease appreciably by a slight reduc-tion as presented in Fig. 2. The net effect of a window on theheat balance of a building depends on the characteristics and ori-entation of the window as well as the solar and weather data. Awindow should have a good light transmittance while providingeffective resistance to heat flow. A low emission coating changesthe behavior of the glass, i.e. heat transmission and heat transmit-tance. The transmittance of the glazing is treated as a linear func-tion in the program although in practice it is changed step-by-stepdue to the commercial offering of different types of windows, see

Fig. 2.Two other important parameters that influence the total use of

energy apart from the building components are the ventilation andthe internal temperature. The airflow is a large source of energyloss, the more air that flows through the building the more energyis used to heat the fresh inlet air. Fig. 3shows that an improvedefficiency of heat recovery in the base-case building of 45% reducesthe annual heating energy use to 6673% depending on location.

The internal temperature influence on the energy use is quiteobvious; the lower internal temperature the less energy has tobe supplied by the heater, see e.g. [40,41]. The internal tempera-ture is regulated by the thermal comfort set by the occupants.

Due to the proportion between the changed internal temperatureand the external temperature difference Lund has a grater gradientthan Ume and Kiruna as shown inFig. 4.

4.3. An investigation of the solar contribution

In this section an investigation of potential savings on the sup-plied space heating based on measures to gain the solar contribu-tion is introduced.

The amount of solar energy that is transmitted through the win-dows depends on the insolation on the collecting surface. Since theenergy use over the lifetime of a building is taken into account, thesolar radiation data should be representative of the average ofmany years. Of course the calculation should take the entire yearinto account, but fine time resolution of the solar radiationis rarelynecessary for this purpose. Hourly variations have very little influ-

ence on annual energy use while they tend to average out espe-cially when they are damped by the thermal inertia of buildings[35]. The directions of the collecting surfaces have a more signifi-cant influence on the energy use than the variations in daily aver-age global solar radiation. InFig. 5the base-case building has awindow at one side only. The building is then rotated around itswindow wall to show the effect of different directions on the an-nual solar gain. When the window wall is directed towards thesouth it will receive twice or more solar gain than for north direc-tion. The difference between the sites is due to the solardeclination.

Different window distributions have different influences on thetotal energy use. The more uniform window distribution at differ-

Table 5

Summary of the result from the simulation of the reference house.

Properties Unit February March

Ground reflectivity 0.70 0.60Transmittance of the direct beam 0.71 0.71Transmittance of diffuse and reflective

radiation 0.61 0.61

Shading factor, SW 0.45 0.69

Global solar insolation, monthly average kW h/m2 0.83 2.23Total solar gain MW h 0.18 0.49Total heat floorloss MW h 0.75 0.78Total energy use for space heating MW h 3.23 2.29

0.70

0.75

0.80

0.85

0.90

0.95

1.00

0 0.1 0.2 0.3 0.4 0.5

Thickness of additional insulation (m)

Changeinannualenergyuse

(Qh,cal/Qh,base)

UmeKirunaLundRoof insulationWall insulation

Fig. 1. Influence of additional roof/wall insulation thickness to the energy use forheating.

0.80

0.90

1.00

1.10

1.20

1.30

1.0 1.4 1.8 2.2 2.6 3.0

U-value of window (W/m2K)

Changein

annualenergyuse

(Qh,cal/Qh,base)

45

50

55

60

65

70

75

80

Perpen

dic

ular

transm

ittance

g(%)

Ume

Kiruna

Lund

g

Fig. 2. Influence of window U-value to the energy use for heating and theperpendicular transmittance,g.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 20 40 60 80 100

Heat recovery efficiency (%)

Changeinannualenergyuse

(Qh

,ca

l/Qh

,base

)

Ume

Kiruna

Lund

Fig. 3. Influence of heat recovery of ventilation to the energy use for heating.



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5. Conclusions

The paper introduced a method that can be used for interactiveinvestigations of buildings energy performance in general and howrefurbishment measures will influence the energy use in particular.The simulation is based on a program called TEKLA. It is using EN832 with an improved procedure to calculate the heat loss through

the floor and the solar heat calculation. The simulation tool hasbeen evaluated on a period of cold and mild weather of a heatingseason, for a common type of single family building in Swedenand found to be reliable for the proposed interactive investigationsenergy savings.

The outcome of the introduced procedure is illustrated ingraphical figures. These figures can give an overview of the build-ing energy-characteristics and can be a support to interactivelyidentify and validate energy refurbishment measures, which willinfluence the energy use.

This particular investigation was conducted for cold climatedata from Sweden. A similar investigation could have been con-ducted on mild or warm climate data. In that context the coolingdemand would be important and the parametric analysis woulddiffer, for example importance of solar shading in the summer.However, despite of the investigated climate conditions and thechosen parameters, this work emphasizes how easily obtainedplots can be used in the early stages of building design for interac-tive investigations of how refurbishment as well as design mea-sures can influence the building energy performance.

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