Scholarly article on topic 'The Framework of an Optimization Model for Building Envelope'

The Framework of an Optimization Model for Building Envelope Academic research paper on "Civil engineering"

CC BY-NC-ND
0
0
Share paper
Academic journal
Procedia Engineering
OECD Field of science
Keywords
{"Building design" / Optimization / "Decision making" / "Computer - based simulation tools" / DesignBuilder / SimaPro / COPRAS}

Abstract of research paper on Civil engineering, author of scientific article — Vilune Lapinskiene, Vytautas Martinaitis

Abstract Building design is an iterative process from the conceptual design up to the final process, so the use of computer-based tools here is vital. The purpose of this research was to investigate the most popular tools for building design and present a framework of an optimization model for building envelope, without compromising on energy efficiency, comfort, cost, and environment. The combination of simulation tools DesignBuilder, SimaPro and the method of multiple criteria complex proportional assessment (COPRAS) were not implemented in researches yet. Following the model, we determine the values, which are usually chosen as the optimization criteria: energy demand (heating, cooling, electricity), comfort parameters (PVM, PPD values, discomfort hours, daylight), embodied, operational energy, CO2 emission, investment and exploitation cost. As the result, the use of this optimization model does not require great experience, but improve and facilitates the building design process.

Academic research paper on topic "The Framework of an Optimization Model for Building Envelope"

Available online at www.sciencedirect.com

SciVerse ScienceDirect Procedia

Engineering

Procedia Engineering 57 (2013) 670 - 677 =

www. el sevi er. com/1 ocate/procedi a

11th International Conference on Modern Building Materials, Structures and Techniques,

MBMST 2013

The Framework of an Optimization Model for Building Envelope

Vilune Lapinskienea *, Vytautas Martinaitisb

abDepartment of Building Energetics, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

Abstract

Building design is an iterative process from the conceptual design up to the final process, so the use of computer-based tools here is vital. The purpose of this research was to investigate the most popular tools for building design and present a framework of an optimization model for building envelope, without compromising on energy efficiency, comfort, cost, and environment. The combination of simulation tools DesignBuilder, SimaPro and the method of multiple criteria complex proportional assessment (COPRAS) were not implemented in researches yet. Following the model, we determine the values, which are usually chosen as the optimization criteria: energy demand (heating, cooling, electricity), comfort parameters (PVM, PPD values, discomfort hours, daylight), embodied, operational energy, CO2 emission, investment and exploitation cost. As the result, the use of this optimization model does not require great experience, but improve and facilitates the building design process.

© 2013 The Authors. Published by Elsevier Ltd.

Selection and peer-review under responsibility of the Vilniu s Gediminas TechnicalUniversity

Keywords: building design, optimization, decision making, computer - based simulation tools, DesignBuilder, SimaPro, COPRAS.

1. Introduction

Thr EU iargri io rrach "nearly zrro-rnrrgy buildings" up io 2020 yrar leads io nrw concrpit of building drtign and construction: io rrach high energy performance, wiihoui compromising on comfort, coti, artihriict and environment

Today, ihr contiruciion indutiry it in ihr early tiaget of a revoluiion io reinveni ihr drtign procett ihai wat uted before ihr adveni of HVAC equipment Drtign iramt including boih archiircit and engineert are formed and ihr building drtign it developed in an iieraiive procett from ihr concrpiual drtign ideat io ihr final deiailed drtign [1].

Thr decitiont made in ihr early tiaget of building drtign have a tirong influence for building't efficiency and performance in further 50-100 yrar, ihai it why, ihr implicaiion of building timulaiion and opiimizaiion ioolt/mrihodt can make ihit procett ratirr and more tuccrttful.

Five main cairgorirt of drtign meihodt and ioolt can be ideniified [1]: drtign procett meihodt/ioolt, drtign tirairgy meihodt/ioolt, drtign tuppori meihodt/ioolt, drtign rvaluaiion meihodt/ioolt and timulaiion ioolt.

In liirraiurr ihr overview of compuier-bated timulaiion ioolt may be found [2-5]. Many auihort iry io couple building performance timulaiion ioolt wiih tome opiimizaiion meihodt or ioolt, io rrach ihr opiimum beiween energy contumpiion and comfort [6], [7], achieve low-rmittion, coti - effective drtign toluiiont [8], zrro-rnrrgy building drtign [9], or minimize ihr life-cycle coti for ihr building't oprraiion [10].

Here, ihr implicaiion of Building Informaiion Modelling (BIM) can lead io more deiailed analytit. [11] pretenied a meihodology ThrrmalOpi for auiomaird BIM-bated muliiditciplinary ihrrmal timulaiion iniended for utr in muliiditciplinary drtign opiimizaiion (MDO) environmenit.

* Corresponding author.

E-mail address: avilune.lapinskiene@vgtu.lt; bvytautas.martinaitis@vgtu.lt

ELSEVIER

1877-7058 © 2013 The Authors. Published by Elsevier Ltd.

Selection and peer-review under responsibility of the Vilnius Gediminas Technical University doi: 10.1016/j.proeng.2013.04.085

[12] used BIM in their method for applying life-cycle analysis (LCA) to early stage decision - making in order to inform designers of the relative environmental impact importance of building component materials and dimensioning choices. The wider overview about implication of BIM into early building design is discussed in [13-15].

Unfortunately most of authors focus on more narrow range of optimization criteria, or choose methods for decision making, which require knowledge and experience working on it.

The goal of this research was to develop an intelligible and clear building envelope optimization model, coupling building performance simulation tool DesignBuilder, the most widely used LCA software SimaPro and using the method of multiple criteria complex proportional assessment (COPRAS) for decision making. Following the framework, more energy efficient, cost effective and environment friendly building design can be achieved.

2. The overview of computer based simulation tools

The most popular computer - based tools (programs), for building energy simulation, life - cycle assessment and optimization are presented in Table 1.

Table 1. Computer-based simulation tools

Program abbreviation

Features

EnergyPlus, DesignBuilder,

TRNSYS,

eQuest,

Energy-10,

DOE-2,

MIT Design Advisor

IDA ICE

EnergyPlus - is a whole building energy simulation program for modelling building heating, cooling, lighting, ventilating, and other energy flows.

DesignBuilder - simulation program for checking building energy, carbon, lighting and comfort performance. Links with all major 3-D CAD software. Includes parametric analysis.

TRNSYS- is a transient systems simulation program with a modular structure. Main applications include: solar systems (solar thermal and photovoltaic systems), low energy buildings and HVAC (Heating, ventilation, air conditioning) systems, renewable energy systems, cogeneration, fuel cells

e-Quest - easy to use, freeware building energy use analysis tool.

Energy-10 - building energy simulation program for smaller buildings, that focuses on the early stages of the architectural design process, and the integration of daylighting, passive solar design, and etc. into high performance building.

DOE-2 - hourly, whole-building energy analysis program calculating energy performance and life-cycle cost of operation.

MIT Design Advisor - is an on-line design tool for architects and building engineers, to give preliminary estimates for the performance of building facades

IDA ICE - a dynamic multizone simulation application for accurate study of thermal indoor climate of individual zones as well as the energy consumption of the entire building.

BEES, Athena GaBi SimaPro,

BEES - evaluates building materials and components Athena - evaluates whole buildings and systems;

GaBi and SimaPro - evaluate the environmental performance of products and services in general at the material, component, and system levels, and include applications to the building industry.

GenOpt, Topgui, LINGO, MCDM-23

GenOpt - is an optimization program for the minimization of a cost function that is evaluated by an external simulation program (EnergyPlus, TRNSYS). Has a library with local and global multi-dimensional and one-dimensional optimization algorithms, as well as algorithms for doing parametric runs.

Topgui - is a pre-processor program for Ole Sigmund's 99 line topology optimization code.

LINGO - a comprehensive tool designed to make building and solving Linear, Nonlinear (convex & nonconvex/Global), Quadratic, Quadratically Constrained, Second Order Cone, Stochastic, and Integer optimization models.

MCDM-23- tool to support decision making in a design process, that requires to take esteem environment, cost, urban context and other various criteria at the same time.

Building simulation is an assistant tool in designing an energy efficient building. There is a possibility to choose from a wide range of building energy simulation tools. One of the most popular - EnergyPlus that engineers, architects use to model energy and water use in buildings, is often used in research studies [16], [17]. It can be coupled with BIM [11], optimization program GenOpt [6].

The design of buildings is a multi-criterion optimization problem, there always being a trade-off to be made between capital expenditure, operating cost, and occupant thermal comfort. Such a design process can be informed by the application

of MCDM techniques. The MCDM process has two elements, the search for viable solutions, and the decision as to which solution is the most desirable [18].

MCDM-23 is the optimization tool, developed by the International Energy Agency (IEA), to support decision making in a design process of a building, for making a selection between two or more candidate design schemes. It is often mentioned as one of optimization tools or used in research studies [19], [20].

GenOpt is an optimization program, that has a library with local and global multi-dimensional and one-dimensional optimization algorithms, as well as algorithms for doing parametric runs. One of its advantage is that it can be coupled with any external energy simulation program (EnergyPlus, TRNSYS and etc.) [21], [22].

As sustainability is an increasingly important part of the building design process, it is essential to include environmental analysis in the early stage of building design. Here the most powerful are computer based tools: SimaPro, BEES, Athena, GaBi. These offer the possibility of analyzing, in detail, a wide range of environmental aspects of materials including embodied energy, gathered in most cases through life cycle inventory analysis [23-25].

3. The method

The goal of presented model is to optimize building envelope components, according the following optimization criteria: energy demand, comfort, CO2 emission, investment and exploitation costs. The principle of our framework is presented in Fig. 1.

Fig. 1. A framework of a model for building envelope optimization

In ihr early tiage of building drtign, ihr goal of archiircit, contiruciort, engineert and clirnit it io find a muiual decition for building drtign toluiion. Here it necettary io arrange ihr prioriiirt for ihr criirria in decition making.

According io ihr pretenied framework, ihr main building drtign and performance timulaiion iool it DetignBuilder. Here a building model it creaied: ihr timulaiion program givet a pottibiliiy io choote proper envelope contiruciion, HVAC parameiert, aciiviiy and tchrdulrt. Thr timulaiion retulit thow energy, comfort, environmenial paramrirrt. In order io optimize ihr building envelope - pottible aliernaiivet thould be reiieraied, or one of DetignBuilder funciiont -paramriric tiudy thould be utrd.

Paramriric tiudy can help io vitualize ihr poirniial of ihr reduction or choote ihr opiimal toluiion regarding ihr contidered criirria (if ihr criirria ihai DetignBuilder offert, are enough.)

In order io optimize building envelope from a life cycle pertpeciive - mairrialt of building contiruciion, oprningt, HVAC equipmeni are analyzed in SimaPro tofiwarr. Thr drirrminaiion of carbon fooiprini it bated on IPCC 2007 meihod, which coniaint ihr climair change faciort of IPCC wiih a timeframe of 100 yeart. Thr drirrminaiion of cumulative energy demand (renewable and non renewable) it bated on Cumulative Energy Demand (CED) meihod.

Thr etiimaiion of invrtimrni and exploration cotit can be done in accordance wiih energy timulaiion retulit (energy contumpiion, energy load).

Thr lati tirp in ihit model it decition making. According io attetted criirria, building envelope aliernaiivet are rvaluaird in ihr decition tuppori mairix. Here a meihod of multiple criirria complex proportional attrtmrni (COPRAS) it utrd. Thit meihod wat firti announced in 1994 [26].

Thr meihod COPRAS attumet dirrci and proportional dependence of ihr tignificance and uiiliiy degree of ihr invrtiigaird vertiont on a tytirm of criirria adrquairly detcribing ihr aliernaiivet and valurt and wrighit of ihr criirria [27]. Thr meihod COPRAS wat applied by many auihort [28-31] in iheir tiudirt.

In ihit cate ihe meihod COPRAS it advaniageout becaute of iit abiliiy io adapi any criieria, which it etteniial: quaniiiaiive and qualiiaiive onrt. In ihit cate, our requiremenit comply wiih ihr timulaiion retulit from DetignBuilder, SimaPro and economical calculaiion.

4. Case study

To thow ihr oprraiion of pretenied model framework, a timple cate it contidered for ihr opiimizaiion of building't exiernal wall. One-zone office building ( 10x20x2,8 m) example for paramriric analytit from ihr DetignBuilder library have been analyzed. Thr locaiion and climair daia wat choten for Liihuania, Kaunat ciiy. Thr aciiviiy, HVAC tchedule it common for an office building. Fig. 2 thowt ihr view of building model.

Fig. 2. Building model in DesignBuilder

To choote ihr beti aliernaiive of exiernal wall, ihree diffrrrni contiruciiont (Aliernaiive 1, Aliernaiive 2, and Aliernaiive 3) have been timulaird. They are pretenied in Table 2.

Table 2.Alternatives of external wall construction

Alternative 1 Alternative 2 Alternative 3

105,00mm Brickwork.-Clutei Leal

External wall section 1 JIJ0R>" Cjjj^frl F

118,20mm XPS Extruded Polystyrene • C02 Blowing 100,00mm Concrete Block (Medium) litii.1 ^'Fr ft^ftne |îï Î VT IOÎ5ÎJTIT Concrete Bte®^ (M edium]

13,00mm Gypsum Plastering(not to scale) !. : Turn G^sÜF^JasleringliiciUo scale]

Heat transfer coefficient: U=0,25 W/m2K Heat transfer coefficient: U=0,35 W/m2K Heat transfer coefficient: U=0,5 W/m2K

Using parametric study, we found the impact of different walls construction to energy, comfort, total CO2 emission. Using SimaPro, carbon footprint and cumulative energy demand (CED) for the materials of wall's construction were determined. Heating, cooling and total energy demand have been converted in to financial terms, according to energy price for heating and electricity from centralized networks. All these results are chosen as the criteria and presented in decision support matrix Table 3.

Table 3. Initial data for decision support matrix

Criteria Unit Weights Alternative 1 Alternative 2 Alternative 3

U value [W/(m2 K)] 0.05 0.25 0.35 0.50

Inertia 0.05 3.15 2.97 0.60

Heating load kWh 0.07 17621.20 17761.51 20422.73

Cooling load kWh 0.05 3822.12 3936.82 3998.62

Total primary energy consumption kWh 0.1 46659.69 46902.44 51008.06

Discomfort (all clo) hr 0.1 650.50 627.00 575.00

Total CO2 0.08 17825.67 17886.18 18692.38

Carbon footprint (1m2 wall construction) kgCO2eq 0.08 71.56 69.38 4.98

CED. Renewable MJ 0.05 60.79 58.57 91.00

CED. Non-renewable MJ 0.03 683.94 657.75 2.92

Price (for 1m2 wall) Lt 0.1 100.00 98.00 274.00

Cost for heating consumption Lt 0.07 4792.97 4831.13 5554.98

Cost for cooling consumption Lt 0.05 1605.29 1653.46 1679.42

Cost for total energy consumption Lt 0.12 6398.26 6484.60 7234.40

In Table 3 the weight values for criteria have been set according to the accepted iscomfort and price (for 1m2 wall), (0.08) for total CO2, carbon footprint (1m2 wall construction), (0.07) for heating load, cooling load, price for heating consumption, (0.05) for U value, inertia, cooling load, (CED. Renewable), (0.03) for (CED. Non-renewable). The total weights should be equal 1. The symbols ,,+" or ,,-" show the higher or lower value of criteria is more useful for us.

To get the best solution from decision support matrix, the following evaluation stages are presented [32].

The formulation of decision support matrix. The aim of this phase - to convert comparison indicators to dimensionless (normalized) values. This way all different unit measurements can be compared.

For this the following formula should be used:

d = X,] dJ

i = 1, m; j = 1, n,

Here : x, -value ,'" of criteria ,,i" in decision value; m - number of criteria; n - the number of compared evaluations; q _ the significance of ,,z" criteria.

The significance criterion qt value is allocated pro rata to all alternative versions a, according to their values x,. Each criterion Xj receives dimensionless weighted values dj, which are equal to the sum of the criteria weights q:

q = Z dj, i = 1, m; j = 1,n. (2)

The summing of minimazing S_, or maximazing S+, normalized weighting values for option ,'", according formula:

m m ---

S+j = I d+lj ; S - j = 2 d_v, i = 1, m; j = 1, n. (3)

i=1 /=1

The sum of S+, and S_, is allways equal to maximazing and minimazing criteria weighting amounts.

n m n n m n ___

= E S+j d+IJ, S- = S S-j =SS d-j, i = 1, m; j = 1,«. (4)

j=1 »=^./=1 j=1

The significance of the alternatives is determined according to them describing positive S+, and negative S_j qualities. The relative significance Qj of each alternative a, is evaluated according formula:

S-min ■ 2 S - j

, +-, j = 1,2,3,...m (5)

+j m S-

S-, ■! -min

Calculation of the utility degree of the alternatives by the following formula:

N, =—¿--100 (6)

The last stage is the determination of priority order of the alternatives. 5. Results

The calculation results for decision making of 3 different external wall constructions are presented in Table 4.

According to the priority order, the external wall construction alternatives ranges as follows: ,,Alternative 1", „Alternative 2" and „Alternative 3".

The assessed criteria values of „Alternative 1" and „Alternative 2" are quite similar, that is why, the utility degree differs only 1 %. The difference between utility degree of „Alternative 1'' and „Alternative 3'' is only 6%, although the difference of U value is 50%. Here is important not only the criteria value, but especially the criteria weight, which can influence the arrangement of the priorities. That is why the determination of criteria weight here is fateful, and complicated. The fewer criteria are analyzed, the more objectively we can set the values.

In order to find the best combination of all building envelope components - analogically all the alternatives ( window, roof etc.) should be evaluated.

Table 4. Decision support matrix for the alternatives of external wall construction

Criteria Unit Weight Alternative 1 Alternative 2 Alternative 3

The assessed dimensionless values of parameters

U value [W/(m K)] ,,- 0.05 0.011 0.016 0,023

Inertia 0.05 0.023 0.022 0,004

Heating load kWh ,,- 0.07 0.022 0.022 0.026

Cooling load kWh ,,- 0.05 0.016 0.017 0.017

Total primary energy consumption kWh ,,- 0.1 0.032 0.032 0.035

Discomfort (all clo) hr ,,- 0.1 0.035 0.034 0.031

Total CO2 0.08 0.026 0.026 0.027

Carbon footprint (1m2 wall construction) kgCO2eq ,,- 0.08 0.039 0.038 0.003

CED. Renewable MJ ,,+ 0.05 0.014 0.014 0.022

CED. Non-renewable MJ ,,- 0.03 0.015 0.015 0.000

Price (for 1m2 wall) Lt ,,- 0.1 0.021 0.021 0.058

Cost for heating consumption Lt ,,- 0.07 0.022 0.022 0.026

Cost for cooling consumption Lt ,,- 0.05 0.016 0.017 0.017

Cost for total energy consumption Lt ,,- 0.12 0.038 0.039 0.043

The sum of maximizing normalized indicators ( '+' S+j) 0.1 0.0379 0.0360 0.0261

The sum of maximizing normalized indicators ( '-' s-j) 0.9 0.296 0.299 0.306

Significance of alternative Qj 1.00 0.34 0.34 0.32

Alternative utility degree Nj 100 99 94

The priority of wall alternatives 1 2 3

6. Discussion and conclusion

Building design is challenge for experts, who are working on the project. As the computer - based tools facilitate this process, the most widely used tools for energy simulation, optimization and LCA assessment have been presented.

The introduced framework of a model is intended to optimize the building envelope, without compromising on energy efficiency, comfort, cost, and environment. The combination of DesignBuilder, SimaPro, and the method of COPRAS was not mentioned in literature yet. The use of these tools and method does not require great experience, but facilitates the building design process.

In this case study a simple example was presented, but the optimization criteria showed only a part of all available from simulation results.

DesignBuilder and SimaPro can submit the simulation results, which are usually chosen as the optimization criteria: energy demand (heating, cooling, electricity), comfort parameters (PVM, PPD values, discomfort hours, daylight), life cycle assessment (CO2 emission). SimaPro lets us not only model products and systems from life-cycle perspective, but also use such features as parameters and Monte Carlo analysis, or a variety of applications, like: carbon footprint calculation, environmental product declarations (EPD), environmental impact of products or services and etc.

Unfortunately, thus far the calculation results of DesignBuilder and SimaPro, can not be automatically processed, so the results should be manually extracted and placed into Excel. As well as the evaluation of investment and exploitation cost.

The biggest advantage of the method COPRAS is the ability to adapt all the criteria that have to be considered. On the other hand, the determination of significance criterion value may have some doubts, which may be solved spending more time analyzing them.

7. Further work

Our further work will focus on the early stage office building design: the optimization of the building envelope, internal comfort, integration of innovative technologies, reduction of CO2 emission during operation and from life-cycle perspective. Here a challenge will be to implement BIM and new optimization techniques into the presented model.

Reference

[1] Heiselberg, P. 2007. Integrated Building Design, DCE Lecture Notes No. 017. Aalborg University.

[2] Wang, S., Yan, C. and Xiao, F. 2012. Quantitative Energy Performance Assessment Methods for Existing Buildings, Energy and Buildings, 55. pp. 873-888.

[3] Sadineni, S.B., Madala, S. and Boehm, R.F., 2011. Passive building energy savings: A review of building envelope components, Renewable and Sustainable Energy Reviews 15(8), pp. 3617-3631.

[4] Zemella, G., De March, D., Borrotti, M., Poli, I. 2011. Optimised design of energy efficient building façades via Evolutionary Neural Networks, Energy and Buildings 43(12), pp. 3297-3302. http://dx.doi.org/10.1016/j.enbuild.2011.10.006

[5] Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K. 2009. Decision support methodologies on the energy efficiency and energy management in buildings, Advances in Building Energy Research 3(1), p. 121-146. http://dx.doi.org/10.3763/aber.2009.0305

[6] Holst, J. N. 2003. Using whole building simulation models and optimizing procedures to optimize building envelope design with respect to energy consumption and indoor environment. In: Eighth International IBPSA Conference, Eindhoven, Netherlands, August 11-14, 2003, pp. 507-514.

[7] Motuzienè, V., Juodis, E. S., 2010. Simulation based complex energy assessment of office building fenestration, Journal of Civil Engineering and Management 16(3), pp. 345-351.

[8] Hamdy, M., Hasan, A. & Siren, K., 2011. Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings, Building and Environment 46(1), pp. 109-123. ign. DCE Lecture Notes No. 017. Aalborg University.

[9] Attia, S., Gratia, E., Herde, A. de & Hensen, J.L.M. 2012. Simulation-based decision support tool for early stages of zero-energy building design, Energy and Buildings 49, pp. 2-15. http://dx.doi.org/10.1016/j.enbuild.2012.01.028

[10] Flager, F., Welle, B., Bansal, P., Soremekun, G., Haymaker, J., 2009. Multidisciplinary process integration and design optimization of a classroom building, Journal of Information Technology in Construction (ITcon) 14, pp. 595-612, http://www.itcon.org/2009/38

[11] Welle, B., Haymaker, J. & Rogers, Z., 2011. ThermalOpt: A methodology for automated BIM-based multidisciplinary thermal simulation for use in optimization environments, Building Simulation, 4(4), pp. 293-313. doi: 10.1007/s12273-011-0052-5

[12] Basbagill, J., Flager, F., Lepech, M., Fischer, M., 2012. Application of life cycleassessment to early stage building design for reduced embodied environmental impacts, Building and Environment. doi: 10.1016/j.buildenv.2012.11.009

[13] Watson, A., 2011. Digital buildings - Challenges and opportunities, Advanced Engineering Informatics 25(4), pp. 573-581.

[14] Schlueter, A. & Thesseling, F., 2009. Building information model based energy/exergy performance assessment in early design stages, Automation in Construction 18(2), pp. 153-163.

[15] Hwang, R.-L. & Shu, S.-Y., 2011. Building envelope regulations on thermal comfort in glass facade buildings and energy-saving potential for PMV-based comfort control, Building and Environment 46(4), pp. 824-834.

[16] Schade, J., Olofsson, T. & Schreyer, M., 2011. Decision making in a model based design process. Construction Management and Economics, 29(4),

pp. 371-382.

[17] Fumo, N., Mago, P. & Luck, R., 2010. Methodology to estimate building energy consumption using EnergyPlus Benchmark Models, Energy and Buildings 42(12), pp. 2331-2337.

[18] Wright, J. A., Loosemore, H. A. & Farmani, R., 2002. Optimization of building thermal design and control by multi-criterion genetic algorithm, Energy and Buildings 34(9), pp. 959-972.

[19] Balcomb, J. D. & Curtner, A., 2000. "Multi-Criteria Decision-Making Process for Buildings", Proc. of 35th Energy Conversion Engineering Conference and Exhibit, July 24-28, 2000, Las Vegas, Nevada.

[20] Saparauskas, J., 2008. "Automated evaluation of alternative solutions of building design". Proc. of the 25th International Symposium an Automation and Robotics in Construction (ASARC-2008), 26-29 June, 2008, Vilnius, Lithuania, pp. 507-514.

[21] Asadi, E., et al., da Silva, M.G., Antunes, C.H., Dias, L. 2012. A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB, Building and Environment 56, pp. 370-378. http://dx.doi.org/10.1016/j.buildenv.2012.04.005

[22] Magnier, L. & Haghighat, F., 2010. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network, Building and Environment 45(3), pp. 739-746.

[23] Hernandez, P. & Kenny, P., 2010. From net energy to zero energy buildings: Defining life cycle zero energy buildings (LC-ZEB), Energy and Buildings 42(6), pp. 815-821.

[24] Crawford, R.H., Czerniakowski, I. & Fuller, R.J. 2011. A comprehensive model for streamlining low-energy building design, Energy and Buildings

43(7), pp. 1748-1756.

[25] Kneifel, J. 2010. Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings, Energy and Buildings 42(3), pp. 333-340.

[26} Zavadskas E. K., Kaklauskas, A. Buildings systemotechnical assessment. Vilnius: Technika, 1996. 280 p.

[27] Turskis, Z., Zavadskas , E.K., Peldschus, F., 2009. Multi-criteria Optimization System for Decision Making in Construction Design and Management. 2009. Engineering economics, 1 (61), pp. 7-17.

[28] Uzsilaitytè, L.; Martinaitis, V. 2010. Search for optimal solution of public building renovation in terms of life cycle, Journal of Environment Engineering and Landscape Management 18(2), pp. 102-110.

[29] Ginevicius, R.; Podvezko, V. 2008. Multicriteria Evaluation of Lithuanian Banks from the Perspective of their Reliability for clients, Journal of Business Economics and Management 9(4), pp. 257-267.

[30] Podvezko, V. 2011. The Comparative Analysis of MCDA Methods SAW and COPRAS. Inzinerine Ekonomika-Engineering Economics 22(2), pp. 134-146.

[31] Chatterjee, P., Athawale, V. M., Chakraborty, S. 2011. Materials selection using complex proportional assessment and evaluation of mixed data methods, Materials & Design 32(2), pp. 851-860.

[32] Zavadskas, E. K., Simanauskas, L., Kaklauskas, A., 1998. Decision support systems in construction. Vilnius: Technika. 236 p.