Smart Grid and Renewable Energy, 2012, 3, 222-230
http://dx.doi.org/10.4236/sgre.2012.33031 Published Online August 2012 (http://www.SciRP.org/journal/sgre)
Optimization of Office Building Façades in a Warm
Summer Continental Climate
Allan Hani, Teet-Andrus Koiv
Environmental Department, Tallinn University of Technology, Tallinn, Estonia.
Received March 28th, 2012; revised July 20th, 2012; accepted July 27th, 2012
ABSTRACT
A typical office building model with conventional use and contemporary building systems was developed for façade
optimization in continental climate. Wall, glazing area and window parameters were taken as the main variables. The
objective function of optimization task described in this article is the minimization of cooling and heating energy con-
sumption. The office building façades optimization was carried out using a combination of IDA Indoor Climate and
Energy 4.5 and GenOpt. The process is described in detail so that the approach may be emulated. A hybrid multidimen-
sional optimization algorithm GPSPSOCCHJ was used in calculation process. The optimization results are presented in
four quick selection charts to assist architects, designers and real estate developers make suitable early stage façade se-
lection decisions.
Keywords: Optimization; Envelope Design; Passive Solar Control; Energy Efficiency; Office Building
1. Introduction
Much research describes building simulation software as
a tool for calculation process. IDA Indoor Climate and
Energy, TRNSYS, Energy Plus, eQuest, DOE-2, etc. are
well-known programs used to create building models and
to perform the necessary energy consumption and indoor
climate condition simulations. These tools have been
tested and validated through real experimental cases. The
simulation tools are usually used to perform limited
numbers of single runs to give an overview and conclu-
sions about a defined task. As these programs are used to
conduct hourly based calculations over the full year, suf-
ficiently accurate energy consumption results are achieved.
The probability for these results to run across the Pareto
frontier optimum solutions is actually very low. A possi-
bility to find optimal solution is to use a “brute force”
search. This method needs a huge calculation resource
due to the fact that all possible combinations are evalu-
ated [1].
A reasonable approach to achieving the optimal solu-
tion is to combine building simulation tools and optimi-
zation software. Optimization software can be custom-
ized for the particular research. Another possibility is to
use an existing solution such as Lawrence Berkeley Na-
tional Laboratory branded GenOpt or Matlab’s Optimi-
zation Toolbox.
Different optimization algorithms are implemented in
optimization software. Generally the algorithms are di-
vided into: single and multi-objective. Selection of the
algorithm depends on the constraints and/or the number
of functions to be optimized. Multi-objective functions
can be solved, for example, with Matlab Optimization
Toolbox, single objective with GenOpt.
Technically the most challenging is to combine simu-
lation and optimization tools. All the earlier studies indi-
cate problems with computational hardware power—the
calculation time is in relation to the number of variables
and functions.
Daniel Tuhus-Dubrow, Moncef Krarti have used
DOE-2, Perl application and Matlab for the optimization
of a residential building envelope shape [2,3]. TRNSYS
and Matlab calculations were done for cooling system
optimization by K. F. Fong, V. I. Hanby, T. T. Chow [4].
Hanna Jedrzejuk, Wojciech Marks used a tailor-made
solution for the optimization of the walls and heat source
for a building [5]. Gianluca Rapone, Onorio Saro had
researched office building shading solutions with a com-
bination of Energy Plus and GenOpt in 2011 [6]. Energy
Plus and GenOpt are combined for indoor comfort and
hydronic heating optimization by Natasa Djuric, Vojislav
Novakovic, Johnny Holst, Zoran Mitrovic [7]. Multi-
layered walls have been optimized with genetic algo-
rithms by V. Sambou, B. Lartigue, F. Monchoux, M. Adj
[8]. Energy Plus and Matlab was used by Jingran Ma, Joe
Qin, Timothy Salsbury, Peng Xu to show the demand
controlled systems economic efficiency in [9]. Weimin

Optimization of Office Building Façades in a Warm Summer Continental Climate 223
Wang, Radu Zmeureanu, Hugues Rivard have published
green building optimization concept with multi-objective
genetic algorithms [10]. M. Mossolly, K. Ghali, N. Ghad-
dar have used Matlab to optimize control strategy for an
air-conditioning system [11]. HVAC system optimization
results were published by Lu Lu, Wenjian Cai, Lihua Xie,
Shujiang Li, Yeng Chai Soh [12]. TRNSYS and GenOpt
thermal comfort has been optimized by Laurent Magnier,
Fariborz Haghighat [13]. VAV system optimal supply air
temperature research was published by Fredrik Engdahl,
Dennis Johansson [14]. Excel and Matlab combination
for building retrofit strategies calculation has been car-
ried out by Ehsan Asadi, Manuel Gameiro da Silva, Carlos
Henggeler Antunes, Luķs Dias [15]. Single and multi-
objective approaches for building façade overall energy
efficiency were demostrated by Giovanni Zemella, Da-
vide De March, Matteo Borrotti, Irene Poli [16]. Energy
conser vation possibilities in buildings have been studied
by V. Siddharth, P. V. Ramakrishna, T. Geetha, Anand
Sivasubramaniam with DOE-2.2 and genetic algorithms
[17]. Multi-parameter thermal optimization (APACHE
software) has been done by A. Saporito, A. R. Day, T. G.
Karayiannis, F. Parand [18]. A comprehensive study of
building energy consumption and indoor environment
optimization was done by Mohamed Hamdy, Ala Hasan,
Kai Siren (Matlab + IDA ICE combination) [19].
Micheal Wetter stated in 2004 the following: “Discus-
sions with IDA ICE developer showed that IDA-ICE
might indeed be a promising tool for use with our opti-
mization algorithms (GenOpt). However, without exten-
sive numerical experiments and code analysis, it is not
possible to conclude that IDA-ICE satisfies our require-
ments” [20].
The current research is based on a combination of IDA
ICE and GenOpt. The IDA ICE and GenOpt combination
has already been used by Ala Hasan, Mika Vuolle and
Kai Siren [21]. Our paper describes the dynamic of win-
dow area, solar factor versus cooling, heating energy
consumption in different cardinal directions. Due to the
fact that the façade energy consumption is evaluated,
other building envelope parameters and internal heat
gains are handled as constants.
Hendrik Voll and Teet-Andrus Kõiv have published an
article about cooling power demand estimation principles
and different parameter relations for commercial build-
ings [22].
Our research is focused on heating and cooling en-
ergy consumption strategies for office buildings. Early
stages of design affect future energy consumption for
the building the most. The objective of this research is to
develop quick selection charts for different cardinal
directions in relation to window area and other envelope
parameters.
2. Methods
2.1. Simulation-Optimization Approach
The theoretical approach for the building shape was cre-
ated in the IDA Indoor Climate and Energy 4.5 environ-
ment. A square shaped three floor model (floor height 3.0
m) is indicated in Fi gure 1.
A typical office building is very often a multi-storey
compact structure. Therefore, calculations were done in
this case for the first floor to eliminate ground and roof
physical effects.
First step, the IDA ICE mathematical model run cre-
ates a substantial ida_lisp.ida file with all defined data
and relations between the parameters. The main structure
of ida_lisp.ida consists of files, constants, tables, mod-
ules, connections, boundaries, start values, integration
and log. To understand the relationships between differ-
ent parameters is technically challenging. The full logic
has to be understood and tested. For example, an increase
of window area must decrease the same face wall area
and vice versa in optimization calculations. As well the
solar factor and shading coefficient have mathematical
relation between them. To create the base IDA ICE
model file for optimization calculations we renamed the
ida_lisp.ida file to templ.ida and modified the envelope
parameters mostly in the modules section. The basic
scheme of the optimization is shown in Figure 2.
The convenient search and study of certain parameters
in the ida_lisp.ida file can be achieved by giving clearly
identified parameter values in the IDA ICE model for the
particular module (glass, wall, etc.). Understanding of the
total ida file puzzle is time-consuming, but unavoidable
for optimization of IDA ICE calculation results with op-
timization tools.
Figure 1. Shape of theoretical calculation model.
Figure 2. IDA ICE and GenOpt simulation-optimization
process.

Optimization of Office Building Façades in a Warm Summer Continental Climate
224
The second step is to create a command.txt file and
define variables for the optimization process. The vari-
ables are indicated in Table 1. GenOpt can handle dis-
crete and continuous variables.
Pre-processing relations between different building
parameters are also described into command.txt file. For
the current paper wall-window and solar factor-shading
coefficient relations were defined (see following code).
Vary { Parameter { Name = A1win; Min = 1.2; Ini =
1.2; Max = 10.8; Step = 1.2; }
Function { Name = A1wall; Function = “subtract (12.0,
%A1win%)”; }
Function { Name = A2wall; Function = “subtract
(11.124, %A1win%)”; }
Parameter { Name = sfGl; Min = 0.2; Ini = 0.2; Max =
0.8; Step = 0.2; }
Function { Name = tGl ; Function = “multiply (0.87,
%sfGl%)”; }
Furthermore, the command.txt file must also contain
information about the optimization algorithm [23].
The actual optimization process was carried out with
GPSPSOCCHJ algorithm. GPSPSOCCHJ is a hybrid
multidimensional optimization algorithm which uses ge-
neralized pattern search (GPS) for the first stage search
and particle swarm optimization (PSO) Hooke Jeeves
algorithm as a fine search for the defined discrete and
continuous variables function solution.
The configuration file idarun.cfg is written only once
and it describes the IDA ICE simulation run parameters.
The third essential file idarun.ini contains information
about the locations of template, input, log, output, con-
figuration and optimization files. The most important
idarun.ini information is the objective function (in our
case minimization function) definition. Constraints can
be set to the optimization function, if necessary. The first
stage of post-processing is also done here. For the dif-
ferent cardinal directions, our study uses the following
IDA ICE templ.ida related code:
Name1 = Energ_kWh;
Function1 = “add(%Cool_kWh%, %Heat_kWh%)”
Name2 = negCool_kWh:
Delimiter2 = “Emeterlocool.Totenergy”;
Name3 = Cool_kWh;
Function3 = “multiply (% neCool_kWh %, -1)”
Name4 = Heat_kWh;
Delimiter4 = “Etelocheat.Totenergy”;
Name5 = WinN_SF;
Function5 = %SFG1%;
Name6 = Win_m2;
Function6 = %A1win%;
Name7 = Wall_m2;
Function7 = %A1wall%;
Our minimization leading function min f(x) is the
minimization sum of cooling and heating energy related
to external wall-glass parameters. The delimited energy
information is recorded separately; therefore we can also
present the balance between cooling and heating indi-
vidually.
Four optimization runs were carried out.
2.2. Outdoor Climate Conditions
The test reference years are widely used for energy per-
formance calculations and indoor climate analysis.
Hourly based outdoor climate data (dry-bulb air tem-
perature, relative humidity, wind speed, direct solar ra-
diation and diffuse radiation on horizontal surfaces for
8784 hours) was used to create the mathematical model
for IDA ICE 4.5 calculations [24]. Comparability of cur-
rent study results for other climatic areas can be done
through monthly and yearly average parameters which
are indicated in Table 2.
2.3. Indoor Environment
Category II requirements from EN 15251:2007 were
taken as the basis for defining indoor climate in simula-
tion-optimization models. This category is considered as
the normal expectation for new buildings and renovations
according to reasonable indoor climate and energy effi-
ciency levels [25].
Table 1. Optimization parameters.
Variable Type Value
Window area
Glass solar factor
Cardinal directions
Continuous
Continuous
-
10% - 90%, step 10%
0.2 - 0.8, step 0.2
North, East, South, West
Table 2. Test reference year parameters.
Month
Air
temperture
˚C
Relative
humidity
%
Wind
speed
m/s
Direct dolar
radition
MJ/m2
Diffuse rdiation
on hozontal surf.
MJ/m2
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Avg
–3.0
–5.2
–0.1
4.0
11.2
14.1
17.2
15.7
10.8
5.8
–0.1
–2.5
5.7
90
89
76
77
70
73
77
81
82
87
91
86
81
5
4
4
4
4
3
3
3
4
4
4
5
4
35.0
93.4
308.1
254.4
493.3
497.8
606.1
453.6
259.0
143.8
68.2
49.7
271.9
39.2
82.0
144.2
190.2
269.6
306.1
290.8
229.7
161.3
82.9
37.0
20.8
154.5

Optimization of Office Building Façades in a Warm Summer Continental Climate
225
Indoor climate comfort can be described by two dif-
ferent indexes: PMV and PPD. These take into account
the influence of six thermal comfort parameters: clothing,
activity, air- and mean radiant temperature, air velocity
and humidity. Table 3 indicates the indoor climate pa-
rameters used for the calculations.
is used from Monday to Friday—in the theoretical calcu-
lations internal heat gains were not estimated for the
weekend.
2.5. Building Envelope and Technical Services
The building enclosure’s U-values were selected to be
challenging but possible to achieve in construction prac-
tice for a “low energy building” [27]. Typical thermal
bridge values have been used in the calculations (the ef-
fect of thermal bridges heat loss achieves more impor-
tance in case superb heat transfer coefficients are util-
ized). HVAC systems and other IDA ICE 4.5 simulation
input parameters are indicated in Table 4.
2.4. Office-Building Conventional Use
Internal heat gains in the average office area are pre-
sented in Figure 3 [26]. The profile and detailed loads
for occupants, equipment and lights were used for calcula-
tions in the IDA ICE 4.5 mathematical model. The profile
Table 3. Indoor climate criteria.
Table 4. Building envelope and HVAC systems parameters.
Indoor environment parameters Constraints
Thermal conditions in winter for
energy calculations 20˚C - 24˚C [21˚C]
Thermal conditions in summer for
energy calculations 23˚C - 26˚C [25˚C]
Personnel insulative clothing ~0.5 clo summer
Personnel activity level ~1.0 clo winter
Airflow to zones ~1.2 met
CO2 level (outdoor 350 ppm) 7 l/s person
[1.4 l/s m2] < 850 ppm
Relative humidity 25% - 60%
Allowed parameter deviation
(working hours) 3%
External wall heat transfer coefficient Uw 0.14 W/(m2K)
Window glass heat transfer coefficient Uwg 0.8 W/(m2K)
Window frame heat transfer coefficient Uwf 2.0 W/(m2K)
External wall/ external wall thermal bridge 0.08 W/K/(m joint)
External window or door perimeter thermal
bridge 0.03 W/K/(m perimeter)
Infiltration q50 1.0 m3/(h m2)
Building wind exposure Semi-exposed
(pressure coefficients)
Air handling unit (AHU) heat recovery 80%
AHU SFP 1.7 kW/(m3/s)
AHU tsupply to zone (tAHU supply = 16˚C) 18˚C
Figure 3. Typical office area internal heat gains.

Optimization of Office Building Façades in a Warm Summer Continental Climate
226
3. Results
GenOpt optimization solver calculated through a total of
658 iterations during four optimization runs for different
façade directions. The calculation and post-processing
results are presented in the following 4 figures (Figures
4-7).
Total yearly specific energy consumption is the aver-
age of four façade selected net energy consumptions
(kWh/m2a).
Figures 4-7 detailed explanation: on primary axis net
energy consumption for façade heating and cooling is
presented. Secondary axis shows the window/wall ratio
in percentages. Horizontal axis show glass solar factor (7
- 8 different window/wall ratio cases for each—see the
blue dots). The selection of optimum starts from the di-
rective window/wall ratio (e.g. from architect)—four dif-
ferent cases are possible for current cardinal direction
related to glass solar factor.
4. Discussion
Window/wall area ratio (indicated in secondary axis)
shall be the primary directive selection parameter (day-
light window design parameters can be taken as addi-
tional constraints to make the first selection for window
area [22]). Energy consumption is directly related to
window/wall ratio and window glass parameters.
In the warm summer continental climate conditions
for North and East façades the solar factor 0.4 can be
suggested due to higher heat energy demand. South and
West façades must have a solar factor as good as possi-
ble (in our case 0.2). High solar factor values must be
prevented for all façade cardinal directions even in a
cold climate.
These quick selection figures (Figures 4-7) can be
used by building architects and developers to make a
first quick-selection of building façades energy con-
sumption. According to the Energy Performance of
Buildings Directions (EPBD) the EU member states
must define nearly zero energy buildings levels. For
new buildings it will be a challenging task to achieve
these levels by 2020, therefore, the current selection
charts provide additional information for early stage
building energy consumption estimation.
4.1. Sensitivity Analysis
To study the sensitivity of the above results we carried
out some single runs for a double skin façade. The same
principles are applicable for different cardinal direc-
tions. The internal envelope must be well thermally
insulated for current climate conditions. Total solar
factor (for both internal and external glazing) shall also
have the suggested values. For double skin façades the
window U-value has a direct effect on energy consump-
tion for windows.
Figure 4. North façade optimization results.

Optimization of Office Building Façades in a Warm Summer Continental Climate 227
Figure 5. East façade optimization results.
Figure 6. South façade optimization results.

Optimization of Office Building Façades in a Warm Summer Continental Climate
228
Figure 7. West façade optimization results.
Future new building solutions will have to follow
nearly zero energy buildings legislation in EU member
states. The current research helps to select office building
façade in the early design stage. Architects often use a lot
of glass in conventional office building façades and this
will result in high energy consumption and life cycle
costs. The energy consumption minimization shall be
done based on the Figures 4-7 in warm summer conti-
nental climate as the figures clearly present the relation-
ship of different parameters (cardinal direction, window
area, solar factor, specific cooling and heating energy
consumption).
Furthermore, Tobias Rosencrantz [28] has published
heating net energy results for façades in the different car-
dinal directions for similar climatic conditions in Sweden
and there is only slight deviation with our results.
Several IDA ICE single runs showed an acceptable
indoor climate with solar factor 0.2 and 0.4.
5. Conclusions
The structure and importance of this work is presented as
follows:
Creation of theoretical office building simulation model
suitable for defining external wall window mathematical
problem for optimization. Hourly based test reference
year parameters were used for the external climate data.
Indoor climate parameters are based on EN15251:2007
and the conventional use of the building [26].
Further research topics should be related to other opti-
mization tools. GenOpt, as currently used, solves single
objective problems. In future, multi-objective solvers
shall be applied to incorporate more detailed indoor en-
vironment (PMV, PPD) considerations into the façade
investigation which has been dealt with here.
The problem of combining IDA Indoor Climate and
Energy building simulation model and the GenOpt op-
timization tool has been overcome. The main steps of
the optimization approach have been described above.
In addition, example code for the optimization files has
been indicated.
To use different double skin façade parameters, com-
bined with thermal comfort, will be another interesting
field of study. Moreover, economical calculations could
be included as one of the multi-objective variables in
further research.
Quick selection charts (Figures 4-7) have been devel-
oped for different façade directions and the results
have been verified. In the quick selection charts heat-
ing, cooling net energy consumption for façades, win-
dow/wall ratio and window solar factor relationships
are indicated.
6. Acknowledgements
Estonian Ministry of Education and Research is greatly
acknowledged for funding and supporting this study.
European Social Foundation financing task 1.2.4 Coop-

Optimization of Office Building Façades in a Warm Summer Continental Climate 229
eration of Universities and Innovation Development,
Doctoral School project “Civil Engineering and Envi-
ronmental Engineering” code 1.2.0401.09-0080 has made
publishing of this article possible.
REFERENCES
[1] P. G. Ellis, B. T. Griffith, N. Long, P. Torcellini and D.
Crawley, “Automated Multivariate Optimization Tool for
Energy Analysis,” IBPSA SimBuild Conference, Cam-
bridge, 2-4 August 2006, 7 p.
[2] D. Tuhus-Dubrow and M. Krarti, “Genetic-Algorithm
Based Approach to Optimize Building Envelope Design
for Residential Buildings,” Building and Environment,
Vol. 45, No. 7, 2010, pp. 1574-1581.
doi:10.1016/j.buildenv.2010.01.005
[3] Y. Bichiou and M. Krarti, “Optimization of Envelope and
HVAC Systems Selection for Residential Buildings,” En-
ergy and Buildings, Vol. 43, No. 12, 2011, pp. 3373-3382.
doi:10.1016/j.enbuild.2011.08.031
[4] K. F. Fong, V. I. Hanby and T. T. Chow, “HVAC System
Optimization for Energy Management by Evolutionary Pro-
gramming,” Energy and Buildings, Vol. 38, No. 3, 2006, pp.
220-231. doi:10.1016/j.enbuild.2005.05.008
[5] H. Jedrzejuk and W. Marks, “Optimization of Shape and
Functional Structure of Buildings as Well as Heat Source
Utilisation Example,” Building and Environment, Vol. 37,
No. 12, 2002, pp. 1249-1253.
doi:10.1016/S0360-1323(01)00100-7
[6] G. Rapone and O. Saro, “Optimisation of Curtain Wall
Facades for Office Buildings by Means of PSO Algo-
ritm,” Energy and Buildings, Vol. 45, 2012, pp. 189-196.
doi:10.1016/j.enbuild.2011.11.003
[7] N. Djuric, V. Novakovic, J. Holst and Z. Mitrovi, “Opti-
mization of Energy Consumption in Buildings with Hy-
dronic Heating Systems Considering Thermal Comfort by
Use of Computer-Based Tools,” Energy and Buildings,
Vol. 39, No. 4, 2007, pp. 471-477.
doi:10.1016/j.enbuild.2006.08.009
[8] V. Sambou, B. Lartigue, F. Monchoux and M. Adj, “Ther-
mal Optimization of Multilayered Walls Using Genetic
Algorithms,” Energy an d Buildings, Vol. 41, No. 10, 2009,
pp. 1031-1036. doi:10.1016/j.enbuild.2009.05.007
[9] J. Ma, J. Qin, T. Salsbury and P. Xu, “Demand Reduction
in Building Energy Systems Based on Economic Model
Predictive Control,” Chemical Engineering Science, Vol.
67, No. 1, 2012, pp. 92-100.
doi:10.1016/j.ces.2011.07.052
[10] W. Wang, R. Zmeureanu and H. Rivard, “Applying Multi-
Objective Genetic Algorithmsin Green Building Design
Optimization,” Building and Environment, Vol. 40, No.
11, 2005, pp. 1512-1525.
doi:10.1016/j.buildenv.2004.11.017
[11] M. Mossolly, K. Ghali and N. Ghaddar, “Optimal Control
Strategy for a Multi-Zone Air Conditioning System Using
a Genetic Algorithm,” Energy, Vol. 34, No. 1, 2009, pp.
58-66. doi:10.1016/j.energy.2008.10.001
[12] L. Lu, W. Cai, L. Xie, S. Li and Y. C. Soh, “HVAC Sys-
tem Optimization—In-Building Section,” Energy and Build-
ings, Vol. 37, No. 1, 2005, pp. 11-22.
[13] L. Magnier and F. Haghighat, “Multiobjective Optimiza-
tion of Building Design Using TRNSYS Simulations, Ge-
netic Algorithm, and Artificial Neural Network,” Building
and Environment, Vol. 45, No. 3, 2010, pp. 739-746.
doi:10.1016/j.buildenv.2009.08.016
[14] F. Engdahl and D. Johansson, “Optimal Supply Air Tem-
perature with Respect to Energy Use in a Variable Air
Volume System,” Energy and Buildings, Vol. 36, No. 3,
2004, pp. 205-218. doi:10.1016/j.enbuild.2003.09.007
[15] E. Asadi, M. G. da Silva, C. H. Antunes and L. Dias,
“Multi-Objective Optimization for Building Retrofit Stra-
tegies: A Model and an Application,” Energy and Build-
ings, Vol. 44, 2012, pp. 81-87.
[16] G. Zemella, D. De March, M. Borrotti and I. Poli, “Opti-
mised Design of Energy Efficient Building Facades via
Evolutionary Neural Networks,” Energy and Buildings,
Vol. 43, No. 12, 2011, pp. 3297-3302.
doi:10.1016/j.enbuild.2011.10.006
[17] V. Siddharth, P. V. Ramakrishna, T. Geetha and A.
Sivasubramaniam, “Automatic Generation of Energy Con-
servation Measures in Buildings Using Genetic Algo-
rithms,” Energy and Buildings, Vol. 43, No. 10, 2011, pp.
2718-2726. doi:10.1016/j.enbuild.2011.06.028
[18] A. Saporito, A. R. Day, T. G. Karayiannis and F. Parand,
“Multi-Parameter Building Thermal Analysis Using the
Lattice Method for Global Optimisation,” Energy and
Buildings, Vol. 33, No. 3, 2001, pp. 267-274.
doi:10.1016/S0378-7788(00)00091-8
[19] M. Hamdy, A. Hasan and K. Siren, “Impact of Adaptive
Thermal Comfort Criteria on Building Energy Use and
Cooling Equipment Size Using a Multi-Objective Opti-
mization Scheme,” Energy and Buildings, Vol. 43, No. 9,
2011, pp. 2055-2067. doi:10.1016/j.enbuild.2011.04.006
[20] M. Wetter, “BuildOpt—A New Building Energy Simula-
tion Program That Is Built on Smooth Models,” Building
and Environment, Vol. 40, No. 8, 2005, pp. 1085-1092.
doi:10.1016/j.buildenv.2004.10.003
[21] A. Hasan, M. Vuolle and K. Siren, “Minimisation of Life
Cycle Cost of a Detached House Using Combined Simu-
lation and Optimisation,” Building and Environment, Vol.
43, No. 12, 2008, pp. 2022-2034.
doi:10.1016/j.buildenv.2007.12.003
[22] H. Voll, T.-A. Kõiv, T. Tark and M. Sergejeva, “Cooling
Demand in Commercial Buildings—The Influence of Day-
light Window Design,” WSEAS Transactions on Applied
and Theoretical Mechanics, Vol. 5, No. 1, 2010, pp. 101-
111.
[23] M. Wetter, “GenOpt(R) Generic Optimization Program,
User Manual, Version 3.0.0,” Lawrence Berkeley Na-
tional Laboratory, Berkeley, 2009. doi:10.2172/962948
[24] T. Kalamees and J. Kurnitski, “Estonian Test Reference
Year for Energy Calculations,” Proceedings of the Esto-
nian Academy of Science, Engineering, Vol. 12, No. 1,
2006, pp. 40-58.
[25] EN 15251:2007, “Indoor Environmental Input Parameters
for Design and Assessment of Energy Performance of

Optimization of Office Building Façades in a Warm Summer Continental Climate
230
Buildings Addressing Indoor Air Quality, Thermal Envi-
ronment, Lighting and Acoustics,” 2007.
[26] Estonian Government Ordinance No. 258, “Minimum
Requirements for Energy Performance of Buildings (20.
12.2007),” RT I 2007.
[27] J. Kurnitski, A. Saari, M. Vuolle, J. Niemelä and T.
Kalamees, “Cost Optimal and nZEB Energy Performance
Levels for Buildings,” Final Report, 2011.
[28] T. Rosencrantz, “Performance of Energy Efficient Win-
dows and Solar Shading Devices, Evaluation through
Measurements and Simulations,” Lund University, Lund,
2005.