Multiple-Criteria Analysis of Life Cycle of Energy-Efficient Built Environment - Nearly Zero Energy Building Refurbishment - page 296

conditions and the constituent parts of the process in question. Moreover, the
realization of some objectives seems more rational from the economic perspective
thought from the other perspectives they have various significance. Therefore, it is
considered that the rationality of life cycle of energy-efficient built environment
depends on the rationality of its composite parts as well as on the ability to satisfy
the needs of the interested parties and the rational character of environment
conditions. Our research object is life cycle of energy-efficient built environment,
interested parties striving to attain their goals and micro-, meso- and macro-
environment making an integral whole.
The structure of this chapter is as follows: Sect. 2 , which follows this intro-
duction, describes the multiple-criteria decision-making. Section 3 analyses the
model for a complex analysis of life cycle of energy-efficient built environment.
Sections 4 and 5 contain case studies. Certain concluding remarks appear in Sect 6 .
2 Multiple-Criteria Decision-Making
A thorough energy-efficient built environment multiple-criteria analysis is quite
difficult to undertake, because a building and its environment are complex systems
(technical, technological, environment, ecological, social, economical, comfort,
esthetical, etc.), where all subsystems influence the total efficiency performance and
where the interdependence between subsystems play a significant role. Many
multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis
(MCDA) methods have been developed in the world for solving the above-men-
tioned and other problems as follows: AHP method (Kablan 2004 ; Nigim et al. 2004 ;
Jaber et al. 2008 ; Alanne et al. 2007 ), COPRAS (Kaklauskas et al. 2005 , 2006 ), Data
envelopment analysis, Decision EXpert, Disaggregation approach (Diakoulaki et al.
1999 ), Displaced Ideal (Mirasgedis and Diakoulaki 1997 ), Dominance-based rough
set approach, ELECTRE (Georgopoulou et al. 1997 ; Beccali et al. 1998 , 2003; Thiel
and Mroz 2001 ), Evidential reasoning approach, Fuzzy sets (Beccali et al. 1998 ;
Cavallaro and Ciraolo 2005 ; Gamboa and Munda 2007 ; Jaber et al. 2008 ; Alanne
et al. 2007 ), Genetic algorithm (Juan et al. 2009 ; Wright et al. 2002 ), Goal pro-
gramming, Grey relational analysis, Information deficiency method (Afgan and
Carvalho 2002 ), Inner product of vectors, MACBETH, Multi-attribute-utility
analysis (Renn 2003 ), PAPRIKA, PROMETHEE (Goumas et al. 1999 ; Haralamb-
opoulos and Polatidis 2003 ; Cavallaro 2005 ), SIR method, TOPSIS, Value analysis,
Value engineering, Value tree method (Renn 2003 ), VIKOR, Weighted product
model, Weighted sum model, PAIRS (Salo and Hämäläinen 1992 ).
The aforesaid methods were used to solve various problems of energy-efficient
built environment:
• ELECTRE (Georgopoulou et al. 1997 ; Beccali et al. 1998 , 2003 ; Thiel and
Mroz 2001 ): regional energy planning, evaluation of renewable energy options,
renewable energy diffusion strategies, renewable energy technologies, selecting
a heating system for a historical building.