A life-cycle approach for multi-objective optimisation in building design: methodology and application to a case study

被引:7
|
作者
Bonamente, Emanuele [1 ,2 ]
Brunelli, Cristina [1 ]
Castellani, Francesco [1 ]
Garinei, Alberto [3 ]
Biondi, Lorenzo [3 ]
Marconi, Marcello [3 ]
Piccioni, Emanuele [1 ]
机构
[1] Univ Perugia, Dept Engn, Perugia, Italy
[2] Univ Perugia, Interuniv Res Ctr Pollut & Environm, Perugia, Italy
[3] Guglielmo Marconi Univ, Dept Sustainabil Engn, Rome, Italy
关键词
Multi-criteria decision analysis; genetic algorithms; building retrofit; building optimisation; sustainability; SOCIAL HOUSING BUILDINGS; ENERGY EFFICIENCY; ENVIRONMENTAL-IMPACT; DECISION-MAKING; RETROFIT; COMFORT; ENVELOPE; SYSTEM; SCENARIOS; ALGORITHM;
D O I
10.1080/10286608.2019.1576646
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A novel approach for the identification of the optimal strategy for building retrofit is presented and applied to a case study in northern Italy. Increased envelope insulation, more efficient heat-generating systems, thermal distribution and terminal units, heating control systems, electric distribution systems, and renewable energies exploitation were considered as possible measures. A tailored multi-criteria decision analysis tool is developed to embrace the entire building life cycle and includes all the possible combinations in a discretized approach, with the aim of optimising conflicting design parameters, such as energy consumption, costs, GHG emissions, and comfort level. The proposed methodology is applied to two scenarios: nominal building parameters, and the validated model obtained using dynamic energy simulations. In both cases, most energy-efficient solutions are found to be optimal, however, large differences in overall energy consumption are found between the two scenarios resulting in different sizing of optimal renewable-energy installations and different electric distribution systems. A sensitivity analysis is also performed to estimate how results respond to the variation of input parameters. Despite a large difference between the two scenarios, the proposed methodology is found to be stable in identifying the optimal solution and offers full customisation features to adapt to different cases.
引用
收藏
页码:158 / 179
页数:22
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