Gene ic algo i hm based app oach o op imizing he
ene gy a ing on exis ing buildings
P ac ical applica ion
This pape p esen s an inno a i e me hod o he building ene gy- e o i p ocess. By
applying a simple gene ic algo i hm, he aim is o op imize he cos o in e ening in an
exis ing building by ixing he ene gy a ing ob ained a a gi en alue.
The p ac ical po en ial o he me hod p esen ed he e is qui e ex ensi e, wi h i s g ea es
exponen being i s use by echnicians who a e un amilia wi h op imiza ion p ocesses.
The applica ion o his calcula ion me hodology would simpli y he s udy o p ojec s in
he phase o selec ing ene gy-sa ing measu es, gi en ha he e a e cu en ly many o
hem, wi h hei independen cha ac e is ics, which makes he selec ion p ocess a slow
and ine ec i e ask.
In addi ion, he me hod’s in ui i e in e ace and he ac ha i is p og ammed in MS
Excel make i an inno a i e me hod wi h g ea applicabili y in he ield o building
p ocess op imiza ion.
Abs ac
The p oblem o imp o ing he ene gy beha iou o exis ing buildings is a cu en opic
o in e es in scien i ic esea ch. In ecen yea s, Public Adminis a ions ha e made an
e o o in oduce no ms ha help o eo ien he endency owa d inc easing ene gy
consump ion by buildings. To do so, manu ac u e s ha e de eloped nume ous ene gy
e iciency measu es ha ha e become widely ex ended. The main p oblem when
selec ing one o a ious measu es is o iden i y he ones ha will p o ide he bes ade-
o be ween se ices and implemen a ion cos s. This pape p esen s a s udy ocused on
implemen ing echniques o calcula ing he hea ing and cooling ene gy demand, along
wi h gene ic algo i hm, o op imize he p ocess o adjus ing he building’s ene gy
e iciency a ing o a de e mined a ing o exis ing building. The p oposed op imiza ion
app oach is applied o a eal case o demons a e i s alidi y in a eal wo ld si ua ion.
Building Se ices Enginee ing Resea ch and Technology
F esco Con e as, Ra ael; Uni e si y o Se ille, G aphic Exp ession and Building Enginee ing
Moyano, Juan; Uni e si y o Se ille, G aphic Exp ession and Building Enginee ing
Rico, Fe nando; Uni e si y o Se ille, G aphic Exp ession and Building Enginee ing
Keywo ds: Gene ic Algo i hm, Ene gy a ing, Building ene gy e o i measu es, Ene gy sa ing
1. In oduc ion
The new policies o comba clima e change, s emming om he es ablishmen o he
Kyo o P o ocol and mo i a ed by he inc ease in ene gy dependence in some egions ( he
Eu opean Union, o example [1]), a e o ien ed owa d educing ene gy consump ion and
he emission o g eenhouse gases in o he a mosphe e. In Eu ope, Di ec i e 2010/31/EU
[2], which eplaced Di ec i e 2002/91/CE (EPBD) [3], es ablishes he miles ones o be
eached in o de o achie e he emission commi men s ag eed upon o he yea 2020.
Buildings ha e an impo an weigh in he o e all compu a ion o g eenhouse gas
emissions, mainly CO2 [2,4]. The cu en legisla ion equi es bo h newly cons uc ed
buildings and, especially, exis ing ones o mee a se ies o minimums wi h ega d o hei
ene gy beha iou . Thus, he e is an a emp o no only de ain he inc ease in he ene gy
demand o he socie y, bu also o educe i . In u n, he Membe S a es o he Eu opean
Union ha e he obliga ion o ake he necessa y measu es so ha he objec i es
es ablished by he communi y no ma i e can be me , including he achie emen o ze o
ene gy buildings [2].
In ecen yea s, he cons uc ion ield has been he objec o nume ous s udies wi h he
pu pose o minimizing ene gy consump ion [5]. Designing building ene gy e o i
s a egies is an a duous ask, gi en ha he building i sel is an o ganism composed o
many sys ems and subsys ems ha di ec ly in luence i s ene gy consump ion [6]. In
addi ion, he ole played by he use s in he beha iou o he ins alla ions is a de e mining
ac o in hei e iciency [7].
Ini ially, in he 1980s, he compu a ional op imiza ion p ocess was applied o he design
o new buildings and hei ope a ional condi ions, in o de o maximize hei e iciency.
La e , in he 1990s and he i s decade o he 21s cen u y, he scope o ac ion o hese
echniques was b oadened o include hei applica ion in he ene gy e o i p ocess o
exis ing buildings [8]. Cu en ly, his p ocess is an al e na i e o designing ene gy
e o i s a egies wi h a high applica ion po en ial, gi en ha in only one s ep we achie e
he maximum esul s o ce ain condi ions, hus acili a ing he wo k o he decision
make .
T adi ionally, di e en ypes o dynamic he mal simula ion so wa e ha e been used,
combined wi h op imiza ion p ocesses. The main incon enience o his me hod is i s high
compu a ional cos , due o he nume ous in e ac ions equi ed o achie e an op imiza ion
ha mee s all he p oposed es ic ions [9].
2. O e iew and backg ound
To achie e he op imiza ion o he ene gy e o i p ocess, we can d aw on a ious
me hods, which, acco ding o [10], a e classi ied as Di ec Sea ch Me hods (Pa e n
Sea ch [11], Linea P og amming [12] and Non-Linea P og amming), E olu iona y
Algo i hms (Gene ic Algo i hm [13], E olu iona y P og amming [14], Gene ic
P og amming [15], Co a iance Ma ix Adap ion E olu iona y S a egy [16] and
Di e en ial E olu ion [17]) and Me a-heu is ic Algo i hm (Ha mony Sea ch [18],
Pa icle Swa m Op imiza ion [19], An Colony Op imiza ion [20] and Simula ed
Annealing [21]).
O all o hese me hods, he mos common is he Gene ic Algo i hm (GA), which uses he
p inciple o na u al selec ion o each he op imal esul . One o he easons o i s
popula i y is ha i is capable o e icien ly handling non-linea p oblems wi h
discon inui ies and a la ge numbe o local minimums and maximums [22].
The GA was i s applied in newly cons uc ed buildings, a ec ing hei o m, spaces,
ma e ials, e c. In [23], Znouda e al. de elop a p ocedu e o op imize he design o
buildings in he Medi e anean a ea. In [24], Rakh and Nassa p esen a me hodology ha
ies o op imize he shape o he oo o achie e uni o mi y in he en ance o sunligh on
a gi en day.
G adually, au ho s ha e been ex ending i s applica ion o he ene gy e o i p ocess,
o ien ed owa d mee ing objec i es such as minimizing ene gy consump ion. Juan Y [9]
shows an in-line decision me hod ha seeks o acili a e he e alua ion o he s a e o he
building, in addi ion o p oposing ene gy building e o i s a egies o achie e a ade-o
be ween he cos and quali y ac o s. Coley and Schuka [25] combine a GA using he
a iables he mal conduc i i y o he wall ma e ials and he he mal ine ia o each a ea
o he building, wi h human alue judgmen s (a chi ec u al a ac i eness). Mazan and
Pin o [26] p esen a me hod ha ies o minimize he ene gy consump ion in hea ing and
he hou s o a i icial ligh ing, h ough he design o shade elemen s. Sahu e al. [27]
design he HVAC sys em (hea ing, en ila ion and ai condi ione ) in a opical clima e
by combining he admi ance me hod and a gene ic algo i hm. Jin and O e end [28]
p esen an al e na i e p ocedu e in which hey y o op imize an ex e io wall by
imp o ing he social, en i onmen al and economic alues o he building, adjus ing i s
execu ion cos . Mala ji e al. [29] use a mul i-objec i e GA o maximize ene gy sa ing
a e ca ying ou an ene gy e o i ing o a building, while managing o minimize he
pe iod o e u n on he ini ial in es men made. Shao e al. [30] p esen a model based on
GA ha ies o op imize he ene gy e o i ing o exis ing buildings by minimizing h ee
objec i es: annual emissions, ope a ional ene gy consump ion and ini ial in es men .
In addi ion o GA, o he op imiza ion me hods ha e been applied in ene gy building
e o i ing, al hough less equen ly. In [31] Asadi e al. apply Tchebyche P og amming
o minimize he building’s ene gy consump ion by modi ying he he mal en elope and
spaces.
The common h ead in all he a icles men ioned is he a emp o op imize ce ain
ac o s, always minimizing he cos o implemen ing he ene gy building e o i
s a egies equi ed o his pu pose.
Using MS Excel [32] p og amming, he p esen s udy simpli y a mul i-c i e ia
op imiza ion app oach in o a GA based mono-c i e ia op imiza ion app oach applied o
he hea ing and cooling ene gy demand a ings, basing on ISO 13790 [33] and EN 15217
[34]. We chose MS Excel because i is a widely used so wa e ha is easy o he a e age
use o handle. The pu pose is o op imize, h ough GA, he execu ion cos s necessa y o
each a ce ain ene gy e iciency a ing o he selec ed building. The p oposed
me hodology is applied in a case s udy o demons a e i s obus ness and unc ionali y.
Rega ding he ene gy building e o i s a egies used in he case s udy, passi e me hods
a e con empla ed, gi en ha he pu pose is o adjus he ene gy demand. The decision
a iables a e he ype o he mal insula ion on ex e nal walls and oo s, as well as hei
hickness, he ma e ial o window ames and glass, and he shade ac o p o ided by
sola p o ec ion de ices.
The nex sec ion o he a icle con inues wi h he o mula ion o he p oblem and he
app oach o esol ing i . In sec ion 4, he p oposed app oach is applied o a eal case.
Finally, sec ion 5 p esen s he conclusions o he s udy and p oposes new lines o
esea ch ha can eme ge om i .
3. The p oposed GA based mono-c i e ion app oach
The me hodology p oposed by ISO 13790 was implemen ed o de e mine he hea ing and
cooling ene gy demands, along wi h EN 15217, e e ing o he ene gy a ing o
buildings, in MS Excel. The main eason we selec ed his so wa e is because i can be
applied easily by echnicians wi h no speci ic knowledge in he a ea o op imizing
p oblems wi h se e al a iables. This s eng hens he each o he me hodology
p esen ed, b inging i close o he p o essional sec o , which is mo e amilia wi h MS
Excel han wi h o he mo e speci ic so wa e like MATLAB.
The GA included in he MS Excel Sol e Tool [35] was used o launch he op imiza ion,
due o he b oad sea ch space ha con empla es his p oblem (see poin 4.2.). I is a
simple-objec i e GA, ha is, a nonde e minis ic me hod. S a ing wi h an ini ial
popula ion (possible combina ions o he con igu ed a iables), i andomly applies he
ope a o s o mu a ion, c osso e and selec ion (based on he p inciples o he Theo y o
E olu ion by C. Da win), which modi y his popula ion and c ea e new indi idual ones
(possible solu ions). These ope a o s a e i e a i ely applied un il he p oblem con e ges,
hus inding he op imal solu ion in his calcula ion p ocess. This solu ion can di e i he
calcula ion is un again, as i is a nonde e minis ic me hod, bu bo h solu ions will be
close o he op imal one.
The calcula ion me hod es ablished by ISO 13790 is con as ed and e i ied, so ha he
esul s ob ained a e alid o s udying buildings in e ms o ene gy.
3.1. Decision p ocess
The decision-making me hod p esen ed is s uc u ed as a p ocess composed o 7 s eps, as
Figu e 1 shows:
S ep 1. The use es ablishes he speci ic ene gy a ing ha he/she wan s o achie e.
S ep 2. Ini ial ene gy a ing, based on he s uc u al, occupa ional and unc ional
cha ac e is ics o he building.
S ep 3. I he esul ing a ing is he one desi ed, no in e en ion will be necessa y;
o he wise, he necessa y in e en ions will be planned.
S ep 4. The inpu s a e es ablished (desi ed ene gy a ing, ene gy building e o i
s a egies…), and he e olu iona y op imiza ion p ocess is begun. The esul
ob ained will be he new ene gy demand alues and hei co esponding
a ing, as well as he ene gy building e o i s a egies selec ed in each case.
S ep 5. Check he a ings ob ained and he cos s associa ed wi h achie ing hem.
S ep 6. I he esul ing a ings a e accep able and he execu ion cos alls wi hin he
limi s es ablished by he use , he p ocess is o e . I any o he h ee
condi ions a e no accep able, he es ic ions a e again es ablished, and he
op imiza ion is launched ano he ime.
S ep 7. Repo on he op imal solu ion, indica ing he modi ica ion made in he
cha ac e is ic ene gy aspec s o he building.
3.2. Decision model
ISO 13790 es ablishes ha he demands o hea ing (QH,nd) and cooling (QC,nd), in
condi ions o con inuous hea ing and cooling, a e de ined by:
gnHgnHh HndHQQQ ,,,,
(1)
h ClsCgnCndCQQQ ,,,,
(2)
Depending on he clima e whe e he analysed building is loca ed, one demand could no
ake in o accoun . Fo example, in No way, only hea ing demand will be aken in o
accoun , as he e is no need o cooling. The opposi e would occu wi h coun ies such as
Panama. In mos cases, bo h demands would be aken in o accoun , as in he s udy
example p esen ed in his pape .
Wi h QH,h and QC,h being he hea ing loads o he hea ing and cooling modes,
espec i ely; ηH,gn and ηC,ls a e non-dimensional ac o s o use o hea ing and cooling
loads; and QH,gn and QC,gn a e he o al calo ie gains in hea ing and cooling sys ems.
Figu e 1. Decision p ocess diag am.
Use s (Desi ed ene gy
e iciency a ing)
Execu ions
cos e alua ion
New ene gy
e iciency
a ing
5
6
Ini ial building condi ion
(Ini ial ene gy e iciency a ing)
Op imized solu ion o building
ene gy e o i p ocess
1
No
No
Yes
Is he ene gy e iciency
a ing sough ?
No
in e en ion
is needed
In e en ion
is needed
Se ing es ic ions and
a ailable and compa ible
cons uc ion echnologies
Gene ic Algo i hm
Is he cooling ene gy
e iciency a ing accep able?
Yes
2
3
4
7
Hea ing
ene gy
demand
Cooling
ene gy
demand
A e execu ions cos
accep able?
Is he hea ing ene gy
e iciency a ing accep able?
No
Yes
No
Yes
VETRh QQQ
(3)
SUNINTgn QQQ
(4)
Whe e QTR is he hea ans e ed by ansmission, QVE is he hea ans e ed by
en ila ion, QINT ep esen s he in e nal gains and QSUN he gains due o sola adia ion. In
he calcula ion o hese ac o s, many a iables a e in ol ed. We ake in o accoun hose
ha ha e he g ea es in luence on he ene gy p o ile acco ding o ISO 13790, ha is, U-
Value o walls, oo s, windows and loo , o sola p o ec ion elemen s, among o he s.
3.2.1. Decision a iables
The mono-c i e ion decision-making p oblem is composed o i e decision a iable
g oups, de ined as:
1. he mal insula ion ypes o acades;
2. he mal insula ion ypes o oo s;
3. ame ype in windows;
4. glass ype in windows;
5. shade ac o .
The mal insula ion ypes o acades and oo s a e de ined acco ding o hei he mal
conduc i i y, k (W/mK), and hei espec i e hicknesses (in me e s). The ma e ials o
window ames and glass a e de ined acco ding o hei U- alue (W/m2K). The shade
ac o is p o ided by s anda d da a om ypical buildings in Andalusia (non-
dimensional).
The de ini ion o a iables is based on he me hodology in oduced by Diakaki e al. in
[36] and [37]. This esea ch di e s om p e ious ones in ha his me hod o ien s he
op imiza ions owa d achie ing a pa icula ene gy e iciency a ing and no only o
op imizing di e en c i e ia.
Including I ypes o he mal insula ion ma e ials o acades, we can es ablish bina y
a iables X1i, wi h i = 1, 2, 3,…, I. The a iables will be de ined by:
0
1
1o he wise
selec ed ype i is ma e ial insula ioni acade
Xi
(5)
1
11
I
ii
X
(6)
In he case o he mal insula ion o oo s, he a iable will ha e simila p ope ies o he
p e ious ones, wi h, in his case, J ypes o he mal insula ion ma e ials. The bina y
a iable will be X2j, whe e j = 1, 2, 3,…, J. F om he abo e, i can be de i ed ha :
0
1
2o he wise
lec edpe j is sea e ial ysula ion mi oo in
Xj
(7)
1
12
J
jj
X
(8)
Fo K ypes o window ames, we can de ine bina y a iables o he ype X3k, whe e k =
1, 2, 3,…, K. The a iables will be de ined by:
o he wise
c ed k is sele ame ypei window
Xk0
1
3
(9)
1
13
K
kk
X
(10)
As in he case o he ames, he e a e a ious window glass ypes. Ha ing L glass ypes,
we can es ablish bina y a iables o he ype X4l , whe e l = 1, 2, 3, …, L. The a iables
will be de ined by:
o he wise
elec ed ype k is si glass
Xl0
1
4
(11)
1
14
L
ll
X
(12)
Finally, o es ablish he sola ac o alues yielded by he shade elemen s, hose classi ied
as “ e ical p o uding obs acles” by ISO 13790 a e selec ed. In his no m, he G6 able
indica es he alues his a iable akes depending on he la i ude whe e he building is
loca ed, he angle o inclina ion o he shading elemen , and he o ien a ion o he su ace
Table 7. Cha ac e is ics o window ame ma e ials (WFM).
k
F ame ypes
The mal b eak
The mal
Resis ance
(m2K/W)
Cos
(€/m2)
1
Aluminium
Yes
0.313
314.91
2
Wood
--
0.455
558.88
3
PVC
(Poly inyl chlo ide)
--
0.455
232.35
Table 8. Cha ac e is ics o window glass ma e ials (WGM).
l
Glass ypes
Ai chambe
hickness (mm)
The mal Resis ance
(m2K/W)
Sola
ac o
Cos
(€/m2)
1
S anda d
6
0.303
0.77
39.70
2
Low he mal emissi i y
(ai )
6
0.400
0.41
113.28
3
Low he mal emissi i y
(a gon)
10
0.714
0.39
120.25
Table 9. Sola p o ec ion elemen o 45ºN la i ude (SPE).
m
O ien a ion
Inclina ion
Shadow
ac o
Cos
(€/m2)
1
Sou h
60º
0.50
112.35
2
180º (no elemen )
0.00
3
Sou h Eas
Eas
60º
0.58
4
180º (no elemen )
0.00
5
Sou h Wes
Wes
60º
0.58
6
180º (no elemen )
0.00
4.3. Resul s: analysis and discussion
As Figu e 1 shows, be o e unning he op imiza ion p ocess i is necessa y o know he
building hea ing and cooling ene gy demand in i s cu en s a e. These alues a e
calcula ed in he de eloped ool, and in his case hey a e 87.2 kWh/m2 o hea ing and
44.7 Kwh/m2 o cooling, ob aining a “G” a ing o hea ing and “D” o cooling. In his
speci ic case, a e he op imiza ion p ocess, an a emp is made o each he bes a ing
possible on bo h demands, a ed a he same le el. In o he wo ds, he in en ion is o
each he poin whe e he demands a e op imal wi h he same ene gy a ing in bo h cases
(Table 10). The sea ch o he “A” a ing is begun. I his is no eached in bo h cases, he
nex s ep is o s udy he “B” a ing, and so on.
Table 10. Demand selec ion c i e ia used in he case s udy. Example o he sea ch o he “A” a ing
Ra ing
Hea ing
Cooling
Valid solu ion?
A
Ok
No
No
No
Ok
Ok
Ok
Yes
Table 11 indica es he con igu a ion gi en o he pa ame e s o he GA used in each o
he op imiza ions pe o med.
Table 11. E olu iona y Sol e Pa ame e s.
P ecision o he es ic ions
0.0001
Con e gence
0.1
Size o he popula ion
700
Ra e o mu a ion
0.075
Random ini ializa ion alue
0
Maximum ime wi hou imp o emen
300
The alue used o he popula ion size, g ea e han no mal in hese cases, s ems om he
ex ensi e sample space ha con empla es he p oblem (236 op ions). On he one hand, he
con e gence p ecision is due o he ac ha he goal is o op imize he economic cos , so
ha a mo e sensi i e con e gence would no make sense. The alue gi en o he o he
Sol e con igu a ion op ions (p ecision o he es ic ions, a e o mu a ion, andom
ini ializa ion alue, and maximum ime wi hou imp o emen ) a e aken based on
successi e op imiza ion cycles, wi h hose shown in able 11 p o iding he bes esul s in
compu a ional e ms.
In a i s s ep, wo op imiza ions we e pe o med, he i s aking in o accoun only he
es ic ions de i ed om adjus ing he hea ing demand and he second e e ing o he
cooling demand. I solu ions a e ound o each o hese, a hi d op imiza ion is ca ied
ou aking bo h es ic ions in o accoun .
The alues aken as limi s o he a ings on bo h demands a e es ablished acco ding o
he clima e zone whe e he building is loca ed. In ou case, as i is loca ed in Se illa, he
alue limi s a e hose applicable o zone B4 (Table 4).
The calcula ion p ocedu e begins by adjus ing bo h demands sepa a ely o he “A” a ing
(OPT 1). Fo cing he hea ing demand, we only ind 6 easible con igu a ions, while he e
a e no solu ions o he i o he cooling demand, so ha he e is no compa ible solu ion.
O he 6 op ions ha mee he es ic ions o he hea ing demand, he op imized one is
numbe 6, wi h a necessa y in es men o 8854.07 €. Table 12 shows he con igu a ion o
each o he compa ible solu ions, while Figu e 3 ep esen s he hea ing demand o each
solu ion in ela ion o he necessa y in es men o each i . As we can see, solu ion 1
shows he g ea es ene gy sa ings, bu i s cos is oo high. In he solu ion we conside
op imal, he ene gy sa ings is less (we a e no op imizing), bu he desi ed a ing is
eached wi h he minimum cos (op imized alue).
Table 12. Solu ions o OPT 1.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
1
9992.27
8508.50
6.9 (A)
31.5 (D)
8
7
3
3
2 – 4 – 6
2
9920.80
8353.80
7.6 (A)
32.5 (D)
8
12
3
3
2 – 4 – 6
3
9742.02
8244.60
8.1 (A)
33.2 (D)
8
11
3
3
2 – 4 – 6
4
9594.60
8262.80
8.0 (A)
33.1 (D)
8
6
3
3
2 – 4 – 6
5
9051.52
8435.70
7.6 (A)
31.6 (D)
12
7
3
3
2 – 4 – 6
6
8854.07
8271.90
8.0 (A)
33.0 (D)
3
6
3
3
2 – 4 – 6
Each op imal solu ion o he ene gy demand le el is in bold i alics.
Figu e 3. Solu ions o he building e o i s a egies (Hea ing, le el A) – OPT 1.
Nex , he demands a e o ced sepa a ely o each he “B” a ing. In his case, we ind
solu ions o bo h cases, shown in Tables 13 – 14 and Figu es 4 – 5, o hea ing (OPT 2)
and cooling (OPT 3), espec i ely. Gi en ha he e a e independen solu ions o i he
demands, he nex s ep is o launch he join op imiza ion o bo h demands (OPT 4),
whose esul s a e shown in Figu e 6 and Table 15.
Figu e 4. Solu ions o he building e o i s a egies (Hea ing, le el B) – OPT 2.
Table 13. Solu ions o OPT 2.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
1
10081.20
7998.90
10.6 (B)
33.4 (D)
6
6
2
3
2 – 4 – 6
2
9591.65
8199.10
9.1 (B)
32.7 (D)
7
12
3
3
2 – 4 – 6
3
9537.57
7853.30
13.1 (B)
32.5 (D)
7
2
3
1
2 – 4 – 6
4
9518.50
8071.70
10.0 (B)
33.2 (D)
7
6
1
3
2 – 4 – 6
5
9329.79
7989.80
9.3 (B)
34.8 (D)
8
5
3
3
2 – 4 – 6
6
9081.98
7998.90
10.6 (B)
33.4 (D)
6
6
3
3
2 – 4 – 6
7
9002.35
8217.30
8.9 (B)
32.7 (D)
2
12
3
3
2 – 4 – 6
8
9001.06
7835.10
10.8 (B)
35.0 (D)
7
5
3
3
2 – 4 – 6
9
8906.85
8135.40
8.6 (B)
33.9 (D)
3
10
3
3
2 – 4 – 6
10
8866.62
7580.30
15.8 (B)
32.8 (D)
1
2
3
1
2 – 4 – 6
11
8847.55
7826.00
12.4 (B)
33.5 (D)
1
6
1
3
2 – 4 – 6
12
8801.40
7698.60
13.6 (B)
33.7 (D)
9
6
1
3
2 – 4 – 6
13
8664.39
7807.80
11.2 (B)
34.9 (D)
2
5
1
3
2 – 4 – 6
14
8608.68
7907.90
12.0 (B)
33.0 (D)
8
6
3
1
2 – 4 – 6
15
8594.92
7880.60
11.8 (B)
33.5 (D)
1
6
3
3
2 – 4 – 6
16
8591.26
7917.00
11.7 (B)
33.2 (D)
2
6
3
2
2 – 4 – 6
17
8532.58
7680.40
14.3 (B)
33.2 (D)
7
6
1
1
2 – 4 – 6
18
8509.61
7644.00
14.4 (B)
33.5 (D)
1
6
3
2
2 – 4 – 6
Table 13. Con inua ion.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
19
8411.76
7853.30
10.7 (B)
34.9 (D)
2
5
3
3
2 – 4 – 6
20
8120.78
7862.40
12.5 (B)
33.0 (D)
3
6
1
1
2 – 4 – 6
21
7868.15
7917.00
11.9 (B)
33.0 (D)
3
6
3
1
2 – 4 – 6
22
7603.34
7625.80
13.4 (B)
34.7 (D)
3
5
3
1
2 – 4 – 6
Each op imal solu ion o he ene gy demand le el is in bold i alics.
Figu e 5. Solu ions o he building e o i s a egies (Cooling, le el B) – OPT 3.
Table 14. Solu ions o OPT 3.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
1
12710.10
8490.30
18.2 (C)
20.4 (B)
4
11
1
2
1 – 3 – 5
2
11946.30
8754.20
15.6 (C)
20.1 (B)
8
12
2
2
1 – 3 – 5
3
11615.40
8235.50
19.6 (C)
21.8 (B)
2
11
1
2
1 – 3 – 5
4
11576.40
8999.90
12.3 (B)
20.7 (B)
7
8
3
3
1 – 3 – 5
5
11549.50
8626.80
16.3 (C)
20.8 (B)
7
12
2
2
1 – 3 – 5
6
11491.10
8726.90
15.3 (B)
20.7 (B)
7
8
3
2
1 – 3 – 5
7
11448.10
8590.40
15.6 (C)
21.9 (B)
2
11
3
3
1 – 3 – 5
8
11362.70
8299.20
18.9 (C)
21.8 (B)
2
11
3
2
1 – 3 – 5
9
11237.50
8808.80
14.8 (B)
20.3 (B)
8
11
1
3
1 – 3 – 5
10
11199.80
8690.50
16.3 (C)
20.1 (B)
8
12
1
2
1 – 3 – 5
11
11032.40
9036.30
12.5 (B)
20.1 (B)
8
12
3
3
1 – 3 – 5
12
10947.10
8754.20
15.6 (C)
20.1 (B)
8
12
3
2
1 – 3 – 5
13
10803.00
8563.10
17.0 (C)
20.8 (B)
7
12
1
2
1 – 3 – 5
14
10683.50
8199.10
19.8 (C)
22.0 (B)
12
11
1
2
1 – 3 – 5
15
10635.70
8908.90
13.2 (B)
20.8 (B)
7
12
3
3
1 – 3 – 5
16
10588.10
8745.10
14.8 (B)
21.0 (B)
7
11
3
3
1 – 3 – 5
17
10478.50
8448.80
17.3 (C)
21.8 (B)
12
12
3
2
1 – 3 – 5
Table 14. Con inua ion.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
18
10046.50
8544.90
18.0 (C)
20.0 (B)
12
8
3
1
1 – 3 – 5
19
9998.94
8362.90
19.8 (C)
20.2 (B)
11
8
3
1
1 – 3 – 5
20
9649.75
8417.50
18.7 (C)
20.7 (B)
12
7
3
1
1 – 3 – 5
21
9602.18
8235.50
20.5 (C)
20.9 (B)
11
7
3
1
1 – 3 – 5
22
9577.86
8226.40
19.7 (C)
21.8 (B)
12
12
3
1
1 – 3 – 5
23
9530.29
8044.40
21.5 (C)
22.0 (B)
11
12
3
1
1 – 3 – 5
24
9528.34
7589.40
27.0 (D)
21.5 (B)
4
7
3
1
1 – 3 – 5
Each op imal solu ion o he ene gy demand le el is in bold i alics.
Figu e 6. Solu ions o he building e o i s a egies (Hea ing and Cooling, le el B) – OPT 4.
Table 15. Solu ions o OPT 4.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
1
14201.60
8918.00
13.6 (B)
20.3 (B)
7
4
2
3
1 – 3 – 5
2
13512.60
8990.80
12.7 (B)
20.4 (B)
8
3
1
3
1 – 3 – 5
3
13018.50
8763.30
15.1 (B)
20.5 (B)
6
4
3
3
1 – 3 – 5
4
12972.40
9127.30
11.6 (B)
20.0 (B)
8
8
2
3
1 – 3 – 5
5
12843.00
8954.40
13.3 (B)
20.2 (B)
12
4
1
3
1 – 3 – 5
6
12542.80
8845.20
14.3 (B)
20.4 (B)
11
4
3
3
1 – 3 – 5
7
12519.40
9054.50
12.0 (B)
20.4 (B)
3
3
3
3
1 – 3 – 5
8
12319.20
8963.50
12.9 (B)
20.5 (B)
12
3
3
3
1 – 3 – 5
9
12225.80
9063.60
12.3 (B)
20.0 (B)
8
8
1
3
1 – 3 – 5
10
11695.80
8845.20
13.1 (B)
21.6 (B)
2
2
3
3
1 – 3 – 5
Table 15. Con inua ion.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
11
11644.50
8936.20
13.5 (B)
20.2 (B)
8
8
3
3
1 – 3 – 5
12
11634.90
8908.90
13.2 (B)
20.8 (B)
7
7
2
3
1 – 3 – 5
13
11587.30
8745.10
14.8 (B)
21.0 (B)
7
7
2
3
1 – 3 – 5
14
11576.40
8999.90
12.3 (B)
20.7 (B)
7
7
3
3
1 – 3 – 5
15
11504.50
8817.90
13.2 (B)
21.8 (B)
12
12
3
3
1 – 3 – 5
16
11495.60
8754.20
14.0 (B)
21.7 (B)
2
2
3
3
1 – 3 – 5
17
11460.60
8790.60
14.9 (B)
20.4 (B)
8
8
3
3
1 – 3 – 5
18
11285.10
8972.60
13.2 (B)
20.1 (B)
12
8
1
3
1 – 3 – 5
19
11247.70
8817.90
14.1 (B)
20.9 (B)
7
7
3
3
1 – 3 – 5
20
11032.40
9036.30
12.5 (B)
20.1 (B)
12
8
3
3
1 – 3 – 5
21
10984.90
8872.50
14.1 (B)
20.3 (B)
11
8
3
3
1 – 3 – 5
22
10888.30
8845.20
13.9 (B)
20.8 (B)
12
7
1
3
1 – 3 – 5
23
10835.90
9009.00
12.2 (B)
20.7 (B)
3
7
3
3
1 – 3 – 5
24
10816.40
8672.3
14.8 (B)
21.8 (B)
12
12
1
3
1 – 3 – 5
25
10764.00
8899.8
12.3 (B)
21.8 (B)
3
12
3
3
1 – 3 – 5
Each op imal solu ion o he ene gy demand le el is in bold i alics.
Table 16 shows he esul s o he op imiza ions, as well as he inal con igu a ion o he
ene gy building e o i s a egies selec ed.
Table 16. Solu ions ha op imize each op imiza ion.
Cos
(€)
Ene gy sa ing
(kWh/yea )
Hea ing ene gy
demand (kWh/m2)
Cooling ene gy
demand (kWh/m2)
WIM
RIM
WFM
WGM
SPE
OPT 1
8854.07
8271.90
8.0 (A)
33.0 (D)
3
6
3
3
2 – 4 – 6
OPT 2
7603.34
7625.80
13.4 (B)
34.7 (D)
3
5
3
1
2 – 4 – 6
OPT 3
9528.34
7589.40
27.0 (D)
21.5 (B)
4
7
3
1
1 – 3 – 5
OPT 4
10764.0
8899.80
12,3 (B)
21.8 (B)
3
12
3
3
1 – 3 – 5
Each op imal solu ion o he ene gy demand le el is in bold i alics.
The lowes compu a ional calcula ion ime was o OPT 1: 10 minu es; while he g ea es
was o OPT 4: 20 minu es.
When he cooling ene gy demand is no aken in o accoun in he op imiza ion, no sola
p o ec ion elemen is selec ed. The opposi e occu s when he cooling ene gy demand
en e s he pic u e, whe e, whe he o no he hea ing ene gy demand is aken in o accoun ,
sola p o ec ions a e selec ed o all he acades. Ano he common ac o in all he
op imiza ions is he window ame ma e ial, wi h he PVC op ion being he mos easible
in all o hem.
The bes possible solu ion in e ms o o e all ene gy sa ings is OPT 4, whe e hea ing and
cooling demands a e adjus ed o he B a ing, ob aining a sa ings o 8899.80 kWh/yea .
As Tables 12 – 16 and Figu es 3 - 6 show, he p oposed p ocess does no ocus on ene gy
sa ing, bu a he on he a ing ob ained, as his is i s objec i e.
The esul s ob ained om he applica ion o he p oposed app oach o calcula e he
solu ions o he case s udy show he iabili y o his me hodology as a ool o suppo he
sea ch o balanced s a egies o e o i exis ing buildings.
5. Conclusions and u u e wo k
One o he main p oblems in checking an ene gy e o i o an exis ing building is he
selec ion o he mos bene icial measu es among a la ge numbe o possibili ies de i ed
om a he e ogeneous se o ma e ials wi h e y di e en cha ac e is ics applied o
di e en pu poses. This p oblem can be esol ed by using a mul i-c i e ia app oach ha
akes in o accoun he possible in insic es ic ions, as well as he in luences among
hem, bu i could become ine icien in i s compu a ional calcula ion.
In his s udy, a mono-c i e ion op imiza ion p ocess based on a combina ion o MS Excel
and GA is p esen ed. The me hod is aimed o each a pa icula ene gy e iciency a ing
o he ene gy demand o cooling and hea ing. The main objec i e is he minimiza ion o
ene gy e o i cos . O he possible objec i es, such as he adjus men o ene gy demand,
a e con igu ed as cons ain s.
The simplici y o he implemen a ion o he me hod de eloped makes i a p oduc wi h
g ea po en ial o use in i s ield o applica ion. In u u e de elopmen s, he ool will be
con igu ed as an open complemen o MS Excel, hus os e ing i s dis ibu ion.
The p oposed app oach was applied o an exis ing case s udy, and he esul s show i s
iabili y o p o iding suppo o decisions in a eal se ing, simul aneously conside ing
all he possible al e na i es. Fu he mo e, wi h his me hodology we we e able o
minimize he compu a ional cos and calcula ion ime o he p ocess compa ed o
complex ene gy simula ion p og ams and mul i-c i e ia op imiza ion p oblem, hus gi ing
i a mo e e sa ile na u e.
In u u e s udies, i would be in e es ing o include cons ain s ela ed o he indoo
he mal com o o indoo ai quali y, along wi h hose applied in his s udy, in o de o
achie e a g ea e scope o ac ion o he echnique.
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