Climate models for the assessment of office buildings energy performance

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Building and Environment 39 (2004) 39–50www.elsevier.com/locate/buildenv

Climate models for the assessment of o"ce buildingsenergy performance

F. Gugliermettia ;∗, G. Passerinib, F. Bisegnaa

aDipartimento di “Fisica Tecnica”, Universit�a degli Studi di Roma “La Sapienza”, Via Eudossiana, Rome 18-00184, ItalybDipartimento di “Energetica” Universit�a degli Studi di Ancona, Via Brecce Bianche, I, 60100, Ancona, Italy

Received 2 October 2002; received in revised form 13 May 2003; accepted 28 May 2003

Abstract

In the last few years many advanced computer packages, characterised by a considerable integration between thermal and visual aspects,were developed to support designers and to study building energy performance, innovative materials and daylight control strategies andsystems. These packages, as a function of their complexity and 3nal use, require di5erent types of outdoor data, ranging from monthly(MTD) or seasonal typical days (STD) to more complex typical meteorological years (TMY).

Both the deterministic and the stochastic components of outdoor data are present in TMYs, while MTDs and STDs take into accountonly the deterministic component. The use of MTDs or STDs produces a sensible reduction of the calculation time, above all appreciablein the 3rst phase of the building design process, although it introduces an element of uncertainty in simulation results due to the absenceof the stochastic component of outdoor data. This uncertainty is not easily predictable, as reported by many authors.

The aim of the present work is to investigate the in9uence of the stochastic component of meteorological data in evaluating o"cebuilding energy performance in Mediterranean climate. The study is performed by an advanced numerical computer package, IntegratedENergy Use Simulation (IENUS), which can process di5erent types of climatic data. Di5erent typologies, systems and space managementsare investigated.? 2003 Published by Elsevier Ltd.

Keywords: Climate models; Meteorological data; Stochastic component; Mediterranean climate; Building energy performance

1. Introduction

In the last few years many calculation methods have beendeveloped to study innovative materials and light and HVACcontrol systems and strategies in order to assess, improve andoptimise building energy performances. They range fromvery detailed computer codes using hour by hour weatherdata with di5erent levels of integration between thermal andluminous aspects, to a large variety of simpli3ed calculationmethods, based on average climatic conditions and suitablealso for hand calculations.

On the one hand, the need for simpli3ed algorithmshas been overcome by the di5usion of fast computers thatmake a common practice the use of simulation models, sofar reserved to research; on the other hand, instead, the

∗ Corresponding author. Tel.: +39-6-4814332; fax: +39-6-4880120.E-mail addresses: [email protected] (F. Gugliermetti),

[email protected] (F. Bisegna).

information of an hour-by-hour simulation for the wholeyear is often redundant when only the overall energy de-mands are required.

At the same time, detailed weather data are available onlyfor a restricted number of sites of industrialised countries,and generally the use of these data in computer programsand simulations could be too expensive and time consumingfor the speci3c purpose.

Advanced programs generally require a statistically sig-ni3cant year of hourly meteorological data, obtained withdi5erent processing on set of historical data, as Typical Me-teorological Year (TMY) or Test Reference Year (TRY).Examples of advanced programs are DOE 2.1 [1], TSBI3[2], DEROB-LTH [3], ADELINE [4], IENUS [5,6], thatprovide indoor spaces analysis by a wide integration ofvisual and thermal aspects. On the contrary, Gugliermettiand Sili method [7], Heatlux’s method [8], Khemlani’sprocedure [9], WINSIM [10], as far as LT [11,12] and CENprEN [13] represent examples of simpli3ed proceduresbased on monthly (MTD) or Seasonal Typical Days (STD).

0360-1323/$ - see front matter ? 2003 Published by Elsevier Ltd.doi:10.1016/S0360-1323(03)00138-0

mailto:[email protected]

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40 F. Gugliermetti et al. / Building and Environment 39 (2004) 39–50

A systematic and comparative analysis performed by IEA[14,15] evidenced that commercial and research simulationpackages produce di5erent energy assessments when appliedto the same indoor spaces. Both di5erent mathematical pro-cedures and required outdoor data types can contribute tothe reported discrepancies, with a respective amount that isnot yet well known.

The presence of the stochastic component in the usedmeteorological data produces variations near average deter-ministic values and is so responsible for the aforementioneddi5erences. The deterministic component is generally due toEarth’s revolution and rotation; it presents periodic 9uctua-tions: for the same place, the homogeneous values of eachoutdoor meteorological variable could not vary from yearto year. The stochastic component instead gives 9uctuationsof meteorological variables values around the deterministicone. The deterministic component leads to hourly data fortypical days (such as MTDs and STDs), while taking intoaccount also the stochastic component leads to typical years(such as TMYs and TRYs), series of 8760 hourly data foreach used meteorological variable representing an averageyear.

Many authors showed the impossibility of forecastingthe e5ect of the stochastic component. Hollands et al. [16]found that ignoring the stochastic component of the ambienttemperature produces negligible errors in the energy eval-uation of residential and commercial buildings, while forColdicutt [17] the stochastic component must be includedon heating load simulation; Boland [18] still obtained op-posite results for process heat systems. Oliveira and DeOliveira Fernandes [19] determined loads by using monthlyaverages of climatic variables instead of hourly values;they realise, but not quantify, the importance of the build-ing thermal inertia; moreover, Loveday and Craggs [20]a"rm that adopting a deterministic approach may neglectsigni3cant e5ects resulting from the energy storage in thestructure; Gugliermetti et al. [21,22] found that data withthe deterministic component (MTD) show higher energyrequirements with respect to the stochastic one (TMY) andthat the di5erence increases whereas the solar radiation isstronger, concluding that MTD data can only be used inHVAC design problems, leaving to TMYs the building en-ergy analysis. Brunello and Schibuola [23] at last proposedan intermediate approach for estimating the seasonal energyrequirement of buildings starting from the daily require-ment calculated in periodic conditions over suitable “typicaldays”.

The aim of the paper is to study the in9uence of thestochastic component in the assessment of non-residentialbuilding energy performance for di5erent room conditions,systems management and features in Mediterranean climate,evaluating the reliability of reduced data set (MTD) versustypical meteorological year (TMY).

The approach is based on a comparative analysis amongyearly energy requirements calculated with the same math-ematical algorithms, but with di5erent outdoor data, in

o"ce building spaces. TMYs [24] and MTDs [25], easilyavailable for many Italian cities, are used as outdoor data.The approach was also improved by developing a newMTD, valuable on the base of available MTDs and TMYs,that permits to reduce the discrepancy in the consideredmonthly and yearly data. The knowledge of the in9u-ence of the stochastic component of meteorological datashows also a practical importance, as it represents a para-meter to evaluate the possibility of using STDs and MTDsin building energy simulations to produce sensible reductionin calculation time.

The study uses the computer package IENUS (IntegratedENergy Use Simulation) [5,6] whose main features, withrespect to other advanced simulation programs, are its possi-bility of using di5erent types of meteorological data (TMYas far as MTD or STD) and of implementing easily di5erentlighting and thermal system controls. The original simula-tion package was validated against calorimetric and energyconsumption measurements performed in o"ce spaces inMediterranean climate. In IENUS, thermal algorithms arebased on a transfer function method [26], while the visualenvironment is pre-calculated by the solar system luminouse"cacy method (SSLE) [27] and by the package Superlite[28]. In this study a lighting control system is implementedin the simulation, to re9ect the up-to-day available technol-ogy applied in o"ce buildings to reach the best performancein arti3cial lighting energy saving [29].

2. Meteorological data

Weather data can be decomposed into two components, adeterministic and a stochastic one. The deterministic compo-nent is derived from the Earth’s rotation and revolution; it ischaracterised by quite predictable diurnal and seasonal vari-ations and can still be decomposed in three sub-components:trend, annual and diurnal periodicity; the stochastic compo-nent instead shows unpredictable variations of the weatherand can be regarded as a random time series. Weatherelements present also cross-correlations to each other,and these statistical characteristics may di5er withtime, having multivariate and non-stationary time seriescharacteristics.

In this paper MTDs developed by CNR [25] and TMYspresented in [24], whose main features are exposed inTable 1, are used to study the in9uence of the stochasticcomponent. Both these types of data are based on hourlyoutdoor values of wind speed and temperatures (dry andwet), and on daily values of horizontal direct and di5usesolar irradiances measured over 20 years in many Italianlocations.

Data of hourly external temperature and relative humiditybelonging to CNR’s MTDs were calculated as the average ofthe corresponding values for every day of the same month,while the Kusuda’s method [30] was used to generate hourlysolar irradiance data.

F. Gugliermetti et al. / Building and Environment 39 (2004) 39–50 41

Table 1Characterisation of used meteorological data

Designation Utilisation Advantages Inconveniences

Typical meteorological year (TMY) Energetic consumption in buildingsand thermal evaluation

Good precision Great computing timeBig volume of data

Monthly typical days (MTD) Solar and HVAC system sizing Time gain Possible over or under estimationof system sizing

De Giorgio’s TMY was obtained selecting real monthsin which external dry bulb temperature values 3t at the bestthe mean values over a 20 years period. The following cor-relations were used to evaluate hourly solar direct and totalhorizontal solar irradiance data:

• “Erbs” [31], for the KT ratio between di5use and totalsolar daily irradiance;

• “Liu-Jordan modi3ed” [32] to obtain hourly di5use solarirradiance values from daily data;

• “Collares-Pereira and Rabl modi3ed” [33] to obtainthe hourly total solar irradiance values from dailydata.

CNR’s MTDs and De Giorgio’s TMYs, also if based onthe same outdoor data, gave di5erent values of the totalmonthly, seasonal and yearly solar irradiances (both di5useand direct) due to the di5erent approach and to the usedmathematical algorithms; some little di5erences can also berevealed among the values of the mean temperatures deter-mined with the two meteorological series of data.

Another MTD (marked here with NEW MTD) was de-veloped in this paper in order to both improve the energybuilding analysis based on mean monthly meteorologicaldata as well as to facilitate the study of the in9uence of thestochastic component in simulations by reducing outdoordata discrepancy.

The NEW MTD was obtained by the average of the hourlyvalues of the De Giorgio’s TMY data, producing so farmonthly typical days that override the problem of havingdi5erent monthly and yearly mean values with respect tothe same investigated TMY. NEW MTD shows intermedi-ate characteristics between the series of only deterministicoutdoor data and the series with both the deterministic andthe stochastic component. As example, temperature and di-rect and di5use solar radiation trends of CNR’s MTD andNEW MTD are reported in Figs. 1–3, for the month ofJanuary.

3. Space characterisation and calculation hypothesis

Results are referred to a typical o"ce room, with condi-tioned adjoining spaces so as to have the same indoor tem-perature of the considered room. Room dimensions are: 9oorand ceiling 5×7 m2, external front and rear walls 5×3 m2,lateral walls 7 × 3 m2.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

2

4

6

8

10

12

14

T[˚

C]

CNR NEW

h

Fig. 1. Comparison between CNR MTD and NEW MTD temperaturetrends in a typical day of January.

0

20

40

60

80

100

120

140

160

180

200

I di

r [W

/m2 ] CNR

NEW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24h

Fig. 2. Comparison between CNR MTD and NEW MTD direct solarradiation trends in a typical day of January.

Window systems are assembled in Al frame with a ther-mal transmittance of 3:97 W=(m2K). The considered win-dow system is 3:15×2:15 m. The window centre is displaced1:80 m above the 9oor and in the axes respect to the exter-nal wall; the window is recessed 0:10 m with respect to theexternal wall surface. The window presents no external ob-structions or overhangs. Some glazing panes are considered.

Room furnishing is ordinary and without carpet. The ther-mal transmittance of the external wall is U=0:50 W=(m2K)while di5erent envelope construction weights are consideredin the range 100–400 kg=m2 of 9oor. The following usage


0

20

40

60

80

100

120

140

160

I di

f [W

/m2 ]

CNRNEW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24h

Fig. 3. Comparison between CNR MTD and NEW MTD di5use solarradiation trends in a typical day of January.

aspects characterise the room:

• People occupancy: three o"ce workers everyday from8:00 AM to 20:00 PM;

• People sensible heat: 50 W=person (50% convective and50% radiative);

• Arti3cial light: 9uorescent lamps with recessed, notvented 3xtures, from 8:00 AM to 20:00 PM when it isrequired by the system control. Lighting system producesabout 0:83 lx for Watt of installed power;

• HVAC running period: everyday from 6:00 AM to21:00 PM;

• Latent loads, ventilation, in3ltration and equipment loadsare not considered;

• Thermostat throttling range: 1◦C.

Three Italian cities (Rome, RM, latitude 42◦, Bolzano,BZ, latitude 46◦, Messina, MS, latitude 38◦) representingtypical outdoor conditions that can be met in Mediterraneanclimate have been analyzed. Bolzano is characterised by acontinental climate typical of Alps and Apennines with coldwinters and great yearly thermal excursions. Messina showsa hot and quite dry climate, in which cooling periods areprevalent respect to heating ones, while Rome is charac-terised by a mild climate with a uniform distribution of coldand hot months.

Three di5erent indoor design dry bulb temperatures areconsidered: 26◦C for the summer period, 20◦C for the winterperiod and 23◦C for the midseason period. Seasonal periodsfor the considered cities are reported in Table 2.

A lighting control system is present in the room and it ismade of:

• a daylight control system that can operate a motorisedinternal curtain to avoid overheating and people glare,on the base of light photosensors signal. The daylightingset-point (DLS) of the controller 3xes the maximum tol-erable solar irradiation. Sensors are inside the room nearthe window and are sensible to solar radiation. Internal

curtain can assume only two positions: completely closedor opened;

• an electric light control system that veri3es the daylightillumination level in order to supply arti3cial light. Thenatural illuminance level is controlled in three di5erentzones equal and parallel to the external wall. In each zonearti3cial light can be dimmed to reach the design lightinglevel (DLL) in response to an arti3cial light set point(ALS).

Internal curtain presents a shading coe"cient SC = 0:4and Tvnn = 50%, while the direct light transmitted di5uselyis Tvndi5 =10%. The angular dependence of the curtain lighttransmission is not taken into account.

The indoor data required from a visual point of view are:internal walls visible re9ectance 50%, 9oor re9ectance 20%,ceiling re9ectance 80%, outdoor ground re9ectance 20%.All re9ectances are assumed as “lambertians”.

Since the calculation of internal natural lighting levels isa time consuming task when a dynamic scenario is consid-ered, illuminance data are pre-processed before entering thethermal calculations. IENUS uses for daylight calculationsa variation of the solar system luminous e"cacy method(SSLE). SSLE is the ratio of the illuminance on a pre3xedpoint to the external horizontal solar radiation per m2 ofunobstructed surface. In IENUS two di5erent reference e"-cacies are considered: SSLEdir and SSLEdif that are, respec-tively, the e"cacy referred to the direct and di5use radiationfor the considered internal space and window with a refer-ence DSA standard glass. SSLEdir and SSLEdif are hourlydetermined by Superlite package for di5erent orientationsand for the central day of each month. SSLEs coe"cientsare referred to a luminous e"cacy of 115 lm=W for thedirect component of the solar radiation, 120 lm=W for thedi5use one. Five inter-re9ections are considered to calcu-late the re9ected component, while the number of the nodesare 3xed in 15 for the window, the near and the front wall,in 21 for the left and right walls and in 35 for the workingplane, placed at 0:8 m above the 9oor. The e"cacy requiredfor the other days of the month is calculated by linear inter-polation from the central days SSLEs. While hourly directand di5use solar irradiations are directly provided by TMYdata, the re9ected component Irif is calculated following theAshrae [34] method.

The visible and solar angular properties required to per-form the simulation are calculated by the package Win-dow 4.1 [35] based on the characteristics of each glazingmaterial.

As the computational process is progressive, it is requiredto assume arbitrary hourly heat extraction values and indoortemperatures when a MTD is processed. In this case, cal-culations are repeated until the results for the next days arethe same, i.e. at that time independent of starting evaluationvalues. When TMYs are in use instead, the iterative com-putational process is limited only to the 3rst day, for whichthe initial conditions are unknown.


Table 2Seasonal period

Seasonal Period BZ RM MS

Winter October, November, December, January,February, March, April

November, December, January,February, March

November, December, January,February

Mid-season May, June, September April, May, October March, April, May, OctoberSummer July, August June, July, August, September June, July, August, September

The required energy for heating, cooling and lighting areconverted into petroleum equivalent tons (tep) through thefollowing conversion factors:

• Cooling system with electric chiller (C): 2:17 ×10−2 tep=GJ (performance COP = 3:2);

• Heating system with gas boiler (H): 3:23 × 10−2 tep=GJ(e"ciency �= 0:8);

• Arti3cial light system (L): 8:68 × 10−2 tep=GJ (ballastfactor BF = 0:8).

4. Results

As the features of the environment and the settings ofthe controlling parameters can in9uence the energy analysisdeveloped with MTD and TMY data, several cases havebeen studied:

(a) Two design lighting levels DLL: 300 and 500 lx.(b) Two glazing systems: both systems are made of 6 mm

clear glass panes with an air gap of 16 mm. The 3rstsystem, marked SF1, with a low-e 3lm in face three,shows a central normal visible transparency of Tnv =0:36, while the solar transparency is Tns = 0:19. Thesecond system, REF1, has Tnv = 0:60 and Tns = 0:78.The thermal transmittances of the resulting window sys-tems are 1:80 W=(m2K) for SF1 and 2:74 W=(m2K)for REF1.

(c) Two arti3cial lighting control regulations: 3-dimmingcontrol strategy and no dimming control strategy;

(d) Several envelope construction weights: from 50 to600 kg=m2 of 9oor.

Two opposite and di5erent approaches can be used toset the daylighting control system depending on the mainfunction attributed to the curtain; it can be operated ei-ther to protect people from direct and indirect glare orto maximise energy saving regardless of the visual envi-ronment. Optimal energy saving values of on/o5 curtaincontrol parameter in Mediterranean climate have beenextensively investigated by Gugliermetti and Bisegna [36]:they individuated daylight set-points DLS in the range 250–600 W=m2 of total solar radiation crossing the glazing sys-tem; optimal DLSs depend mainly on window orientationand latitude, also if little variations of the total energy re-quirements are produced by strong changes of DLSs around

their optimal values (see also [37]). On the contrary, morecomplex and less analysed is the visual environment ap-proach; some numerical analyses [38] suggest DLS valuesof 3rst approach to be about 30 W=m2 of direct solar radia-tion crossing the glazing system if direct glare is considered.

In the following analysis both the approaches to 3xthe DLS value are considered; besides, the arti3cial lightset-point ALS is always 3xed equal to the design lightinglevel DLL.

4.1. CNR’s MTD and De Giorgio’s TMY: DLS visualcomfort approach

Reported results are referred to an envelope construc-tion weight of about 200 kg=m2 of 9oor, to the SF1windowsystem and to a 3-dimming control strategy, with DLS =30 W=m2.

Total energy requirements, expressed in tep (equivalenttons, 103 kg, of petroleum), with their partial components(cooling, heating, lighting expressed in GJ) are reported inFigs. 4–7 for the three cities and for di5erent orientations(south, S, west, W, and north, N) when DLL = 300 lx.

Cooling requirements calculated with CNR’s MTD dataare always higher than those obtained with De Giorgio’sTMY data and the di5erences increase with latitude, as re-ported in Fig. 4; for example, the relative percentage di5er-ences (PD) between MTD and TMY, with respect to TMY,evaluating cooling requirements are 23.8% for BZ, 17.5%for RM and 16.5% for MS with the window facing south. Onthe contrary, heating energy requirements calculated withthe considered TMY are always quite bigger in respect tothose obtained with MTD and they increase, with their dif-ferences too, with the increasing of latitude, Fig. 5. The lowtransparency of the glazing surface makes more apprecia-ble the thermal loads due to the lighting system, where ex-ternal climatic conditions are less severe, and reduces thedi5erences among the cities in the evaluation of the energyrequirements of the lighting system, as reported in Fig. 6.

The opposite and balancing trends of the cooling andheating components of energy requirements tend to hide thee5ect of the deterministic component in computing the to-tal energy requirements presented in Fig. 7 for the MTD-and TMY-based approach; in this case PD for the south ori-entation becomes −5:1% for BZ, 3.0% for RM and 8.9%for MS.


Cooling

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.000 0.100 0.200 0.300 0.400 0.500 0.600

TMY [GJ]

MT

D [G

J]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 4. Cooling energy requirements (DLL = 300 lx).

Heating

0.000

0.100

0.200

0.300

0.400

0.500

0.600

MT

D [G

J]

0.000 0.100 0.200 0.300 0.400 0.500 0.600

TMY [GJ]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 5. Heating energy requirements (DLL = 300 lx).

Results in Figs. 8–10 are referred to a DLL of 500 lxand, respectively, report cooling, heating and lighting com-ponents of energy requirements, while total energy require-ments, expressed in tep, are presented in Fig. 11. In thiscase lighting energy requirements become the leading factorand the di5erences among yearly energy requirements fordi5erent cities and types of the outdoor data increase withrespect to DLL = 300 lx, with an increasing in lighting andcooling and a decreasing in heating requirements. With thewindow facing south, i.e., PDs of total energy requirementsare 9.0% for BZ, 13.2% for RM and 13% for MS, while forthe cooling component are 35.5% for BZ, 22.3% for RMand 15.6% for MS.

In order to improve the knowledge of the arti3cial light-ing system in9uence on energy consumption, a case with

Lighting

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.000 0.100 0.200 0.300 0.400 0.500 0.600

TMY [GJ]

MT

D [G

J]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 6. Lighting energy requirements (DLL = 300 lx).

Total Tep

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050

TMY [tep]

MT

D [

tep]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 7. Total energy requirements (DLL = 300 lx).

curtains always opened for several DLLs is reported inTable 3, when TMY data are used and the window facingwest; very similar yearly energy requirements are achievedfor all the cities when DLL is 500 lx, being lighting the mostimportant component.

The yearly working hours in which the curtain are in theclosed position are reported in Table 4 in order to evaluatethe di5erent way of working of the daylight control systemfor the two considered approaches; for the MTD approachthis yearly time period is calculated multiplying the resultsreached for each monthly typical day for the correspondingnumber of days of each month. This last parameter resultsinsensible to DLL values, as the controlling parameter isALS, and show only a small dependence on latitude whenMTD data are used; on the contrary the e5ect of latitude


Cooling

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

TMY [GJ]

MT

D [G

J]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 8. Cooling energy requirements (DLL = 500 lx).

Heating

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

TMY [GJ]

MT

D [

GJ]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 9. Heating energy requirements (DLL = 500 lx).

cannot be ignored in TMYs. TMYs produce di5erent resultsfor the considered cities, as for the other window orienta-tions, although the importance of the solar radiation is re-duced by the increasing of DLL.

4.2. CNR’s and NEW MTD versus De Giorgio’s TMY:DLS energetic approach

Reported results are referred to an envelope constructionweight of about 200 kg=m2 of 9oor, to SF1 window systemand to a 3-dimming control strategy, with the optimal ener-getic values of DLSs reported in [36] and DLL = 500 lx.

Comparisons between CNR MTDs and TMY and be-tween NEWs and TMY data are presented, respectively, in

Lighting

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8TMY [GJ]

MT

D [

GJ]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 10. Lighting energy requirements (DLL = 500 lx).

Total Tep

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

TMY [tep]

MT

D [

tep]

RM-N RM-S RM-W

BZ-N BZ-S BZ-W

MS-N MS-S MS-W

Fig. 11. Total energy requirements (DLL = 500 lx).

Figs. 12 and 13 only for the case of RM, for di5erent orien-tations. Results show how CNR’s MTD data produce highervalues always with respect to TMY, and this is mainlydue to the solar deterministic components. Fig. 13 con3rmthis conclusion, because NEW MTD data also present avery similar trend with respect to TMY. PD of energy re-quirements between CNR MTDs, NEW MTDs and TMYdata for the four main orientations in RM are reported inTable 5.

The choice of the DLS on energetic basis, with respectto visual ones, does not always produce a reduction in theapproximation of energy calculations between MTD andTMY data approaches: for example, orientation South, withreference to Table 5 and Fig. 11, the PD changes from


Table 3Energy requirements for window facing west with curtain always opened

Energy use Bolzano Roma Messina

DLL DLL DLL DLL DLL DLL DLL DLL DLL0 lx 300 lx 500 lx 0 lx 300 lx 500 lx 0 lx 300 lx 500 lx

Cooling (GJ) 2.06 1.66 3.88 2.89 2.56 5.51 3.70 3.48 6.75Heating (GJ) 4.15 2.16 0.88 1.56 0.61 0.14 0.71 1.55 0.03Lighting (GJ) 0.00 2.88 6.45 0.00 2.64 6.14 0.00 2.59 6.07Total (tep) 0.18 0.36 0.67 0.11 0.30 0.66 0.11 0.30 0.67

Table 4Yearly working hours with the curtain in its closed position

Orient. BZ RM MS

MTD TMY MTD TMY MTD TMY

South 2433 1732 2443 1881 2337 1748West 1431 1076 1462 1272 1493 1362North 0 46 0 73 0 96

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0 0.010 0.020 0.030 0.040 0.050 0.060 0.070

TMY [tep]

MT

D [

tep]

South West

North East

Fig. 12. CNR MTD versus TMY for RM.

13.2%, for the visual approach, to 3.5%; an opposite situ-ation exists for west orientation, where PD changes from5.1% to 8.5%. Only the north orientation shows the samePD behaviour of about 8.1%, independent of the value usedto control the incoming of natural light.

NEW MTD can be a more valid substitution of TMYdata with respect to CNR’s MTD in building simulation asit reduces not only the PD of total energy requirements, asreported in Table 5, but also the PD of cooling, heating andlighting components, as reported in Figs. 14–16. This re-sult can be attributed to the calculation of the average ofthe hourly values of climatic data performed on De Gior-gio’s TMY, that produced a levelling of the hourly values

0.0000.000

0.010

0.010

0.020

0.020

0.030

0.030

0.040

0.040

0.050

0.050

0.060

0.060

0.070

0.070

NE

W [

tep]

South West

North East

TMY [tep]

Fig. 13. NEW MTD versus TMY for RM.

Table 5PD case of RM

Data type South West North East[%] [%] [%] [%]

CNR MTDs 3.5 8.5 8.0 7.5NEW MTDs 1.7 3.5 0.2 3.4

and meteorological data series, representing so far a middlecourse between CNR’s MTDs, same structure, and TMYs,same consideration of the stochastic component, althoughpresented with mean values.


0.0000E+00

1.0000E-01

2.0000E-01

3.0000E-01

4.0000E-01

5.0000E-01

6.0000E-01

7.0000E-01

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

TMY [GJ]

MT

D [

GJ]

CNR New

Cooling

Fig. 14. Cooling energy requirements: a comparison between CNR andNEW MTDs in respect to TMY for RM.

Heating

0.0000E+00

5.0000E-03

1.0000E-02

1.5000E-02

2.0000E-02

2.5000E-02

3.0000E-02

3.5000E-02

4.0000E-02

4.5000E-02

5.0000E-02

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

TMY [GJ]

NE

W M

TD

[G

J]

CNR New

Fig. 15. Heating energy requirements: a comparison between CNR andNEW MTDs in respect to TMY for RM.

4.3. Room features analysis

This analysis has been developed utilising NEW MTDsdata, to understand if they are susceptible to be used insteadof TMYs also with di5erent room and control systems char-acteristics. The analyses presented here are limited to southoriented rooms with a DLS energetic approach.

Figs. 17–19 show the di5erent behaviour varying roominertia, arti3cial light regulation and window system.

Speci3cally, Fig. 17 shows the good approximation andreliability of NEW MTD data with respect to TMY fordi5erent room inertia when DLL = 500 lx. Di5erent iner-tia provides di5erent energy consumption due to the energystorage capacity of the structure: its increase causes in fact

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

TMY [GJ]

0.0000E+00

5.0000E-02

1.0000E-01

1.5000E-01

2.0000E-01

2.5000E-01

3.0000E-01

3.5000E-01

4.0000E-01

4.5000E-01

5.0000E-01

MT

D [

GJ]

Lighting

CNR New

Fig. 16. Lighting energy requirements: a comparison between CNR andNEW MTDs in respect to TMY for RM.

0.0000.000

0.010

0.010

0.020

0.020 0.030 0.040 0.050 0.060

0.030

0.040

0.050

0.060N

EW

[te

p]

BZ 100 kg/m2 BZ 400 kg/m2

MS 100kg/m2 MS 400 kg/m2

RM 100 kg/m2 RM 400 kg/m2

TMY [tep]

Fig. 17. NEW MTD versus TMY with several room inertia for the threecities.

a reduction of the external climatic 9uctuations, with lowerloads transferred into the room and consequentially a reduc-tion of HVAC system engagement. In9uence of structureon total energy requirements and on the di5erences betweenNEW MTD and TMY are shown in Fig. 20 in terms ofPD. Moreover, results provide evidence that latitude doesnot seem to in9uence the analysis when an optimal valueof daylight regulation from an energy saving viewpoint op-erates; this means that NEW MTD data well approximatesthe behaviour of TMY data, independent of the latitude.

Fig. 18 on the other side shows the e5ect of a di5erentlight regulation on the energy consumption of the room,with two di5erent inertia. When a no dimming regulationis active, energy requirements double, due to a simpler and

48 F. Gugliermetti et al. / Building and Environment 39 (2004) 39–50N

EW

[te

p]

100 kg/m2of floor; no dimming

400 kg/m2 of floor; no dimming

0.0000.000

0.010

0.010

0.020

0.020

0.030

0.030

0.040

0.040

0.050

0.050

0.060

0.060

0.070

0.070

0.080

0.080

0.090

0.090

0.100

0.100

TMY [tep]

Fig. 18. NEW MTD versus TMY for RM with di5erent light regulation.

REF1 100kg/m2 of floor

REF1 400kg/m2 of floor

0.0000.000

0.010

0.010

0.020

0.020

0.030

0.030

0.040

0.040

0.050

0.050

TMY [tep]

NE

W [

tep]

Fig. 19. NEW MTD versus TMY for RM with di5erent room inertia andwindow system.

less 9exible system; in this case, NEW MTD data show alower accuracy in approximating TMY calculations. Thiscan be explained because the lower 9exibility of the systemincreases the importance of the solar radiation on energyevaluations: di5erent values of radiation cause a di5erentreaction of the shading system, a di5erent lux distribution inthe room, and so, 3nally, a di5erent behaviour of arti3ciallight system.

Fig. 19 shows what happens with a di5erent window sys-tem; it seems that there is a lower accordance between thetwo kinds of data: the reason is strictly linked to the greaterimportance of the external solar radiation, especially the partof it entering the room: SF1 in fact is a solar 3lter windowsystem with a lower transparency with respect to REF1, and

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

0 100 200 300 400 500 600

PD

[%

]

Inertia [kg/m2 of floor]

Fig. 20. Percentage di5erence (PD) of total energy requirements betweenNEW MTD and TMY data varying inertia.

so far is more capable of reducing the e5ect of externalradiation.

5. Some considerations

Three types of meteorological data have been studied try-ing to understand the e5ective importance of the stochasticcomponent in the evaluation of the yearly energy demandof a typical o"ce room in Mediterranean climate, a MTD, aTMY, and NEW MTD, that is a mid-term between the 3rsttwo set of data.

A MTD, generally useful in design problems because itis characterised by mean values deriving from a statisticalanalysis of historical series of climatic data, presents therelevant advantages to be easily available and a kind of“reduced data”. The 3rst aspect is obviously fundamental;the second is very important, as it leaves the possibility todevelop fast simulations that means a sensible gain in termsof time and costs. On the other side, the basic disadvantageis linked to the simpli3cation introduced by the operationof average: it introduces in fact a deterministic approach,ignoring so far the stochastic component, responsible forsensible 9uctuations near the average values. This couldlead to over/under estimations in the evaluation of thermaland cooling loads and consequently of system sizing; more,it surely causes drastic approximations in the evaluation ofbuilding energy analysis.

A TMY is a very complex set of data generally used forbuilding energy analysis: it is a series of 8760 hourly dataand preserves the e5ect of the stochastic component. Thisassures on one side a good precision in the calculations, buton the other a great computing time and cost, that could notbe justi3ed depending on the level of the design process,and a big volume of data, generally di"cult to manage andnot necessary in most problems.

The last set of data is an example of how it is possible tocreate data devoted to speci3c evaluations. NEW MTDs area simple average of TMY data: the calculation of the averagesurely reduces the in9uence of the stochastic componentpresent in TMY, but anyway its e5ect is not deleted.


In this paper a possible approach to the problem of gen-erating adequate data for design and building energy analy-sis is only presented; anyway, it has been shown that NEWMTDs contain the same simple structure of MTDs and so allthe advantages linked to the reduction of simulation time,as well as the stochastic element of TMYs, as it would bea genetic heritage derived and mitigated by the average cal-culation. As shown, NEW MTDs present a very good be-haviour with respect to the standard MTDs, reducing theapproximations with respect to TMYs to values lower than10% (see Table 5), considering both total energy require-ments and partial components of consumption.

NEW MTDs cannot be considered yet as a de3nitive so-lution to the problem: they tend to lose in accuracy on en-ergy evaluations each time the in9uence of solar radiationgrows; the only exception to this consideration is linked tothe proved independence of these new data from latitude.Speci3cally, the in9uence of the lighting system manage-ment and the transparency of the window system have beenstudied: it has been shown that simpler and less 9exible isthe system (i.e. no dimming lighting control, REF1 win-dow), greater is the importance of external solar radiation.As a matter of the fact, this kind of data could be used fornon-residential buildings realised with energy saving crite-ria and light control systems; some speci3c considerationsshould be still developed to individuate the limits of valid-ity linked to area and optical transmittance of the window:an increase of one of these two parameters, if not balancedby a decrease of the other, or of both in fact would cause amore sensible e5ect of sun on the room and consequently ahigher approximation. NEW MTDs are to be considered asnot adequate instead for residential buildings.

6. Conclusions

An analysis of the e5ect of the stochastic componentis presented in this paper, with the aim to understand thereliability of reduced data (monthly typical days, MTDs),characterised by only the climatic deterministic component,versus more complex series of one year hourly data (typi-cal meteorological years, TMYs), taking into account boththe deterministic and the stochastic component for an o"cebuilding in Mediterranean climate.

Solar irradiance has the most relevant in9uence on to-tal yearly energy requirements with o"ce buildings: datawith only the deterministic component (CNR’s MTD) showhigher energy requirements with respect to the stochasticones (De Giorgio’ s TMY), and the di5erence increaseswhereas the solar radiation is stronger. This e5ect is alsoevident for the considered window, SF1, that attenuates theheat storage, reducing the in9uence of the stochastic com-ponent, due to its low transparency. The e5ects of climaticdata, however, lose signi3cance in winter, because of theimportance of the internal heat gain due to arti3cial light-ing system; so the in9uence of external wall orientation is

basically linked to the daylighting factors. The in9uence ofclimatic data is more sensible for the lower thermal e5ectof lighting system, as also reported by Boland [18] for res-idential buildings.

The di5erence between MTD and TMY yearly solarirradiance values do not justify di5erences among the en-ergy requirements found with stochastic and deterministicweather data. It has been so showed that a building inte-grated energy analysis must be developed with TMYs data,although it can be time consuming and not cost e"cient.

Then, a new form of reduced data obtained from a TMYhas been realised and tested, showing a better approxi-mation of TMYs results in respect to previous CNR’s MTD.With these new data the in9uence of structure inertia, ofglazing system transparency and of di5erent lighting con-trol systems, with “optimal energetic” set-point values havebeen investigated to outline the role played by the stochas-tic component of outdoor data in building energy analysis.Results obtained varying some parameters showed thateach time the importance of the solar radiation on energyevaluations grows, be the reason the lower 9exibility ofthe arti3cial lighting system, or the di5erent transparencyof the window system, or still the latitude, there is a loweraccordance between the two kinds of data.

It is so possible to conclude that generally only TMYsmust be taken into account for the integrated energy analysisof o"ce buildings to avoid the risk of overvaluating the en-ergy requirements, and every e5ort to substitute them mustbe done carefully by creating “adhoc” MTDs for the con-sidered problem, as it can be evinced by the di5erent, andsometimes contrasting, results available in bibliography.

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