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

• Published in: Civil engineering and environmental systems Vol. 35; no. 1-4; pp. 158 - 179
• Main Authors: Bonamente, Emanuele, Brunelli, Cristina, Castellani, Francesco, Garinei, Alberto, Biondi, Lorenzo, Marconi, Marcello, Piccioni, Emanuele
• Format: Journal Article
• Language: English
• Published: Basingstoke Taylor & Francis 02.10.2018; Taylor & Francis Ltd
• Subjects: Building design; building optimisation; building retrofit; Case studies; Computer simulation; Control systems; Decision analysis; Design optimization; Design parameters; Distribution; Energy; Energy consumption; Energy costs; Energy efficiency; Exploitation; genetic algorithms; Greenhouse gases; Heating; Insulating materials; Insulation; Life cycle; Life cycle analysis; Life cycles; Methodology; Multi-criteria decision analysis; Multiple criterion; Multiple objective analysis; Parameters; Renewable energy; Sensitivity analysis; sustainability
• ISSN: 1028-6608; 1029-0249
• DOI: 10.1080/10286608.2019.1576646

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.