Implementing multi objective genetic algorithm for life cycle carbon footprint and life cycle cost minimisation: A building refurbishment case study. (15th February 2016)

• Record Type: Journal Article
• Authors: Schwartz, Yair; Raslan, Rokia; Mumovic, Dejan
• Is Part Of: Energy. Volume 97(2016)
• Page Start: 58
• Page End: 68
• Publication Date: 2016-02-15
• Subjects: Life cycle carbon footprint -- Life cycle cost -- Life cycle analysis -- Genetic algorithms -- District heating; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.11.056
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Early design decisions made by architects have been shown to significantly impact the energy performance of buildings. However, designers often lack the resources or knowledge to take informed decisions that might improve building performance. The refurbishment of existing buildings is considered to significantly contribute to the reduction of the life cycle environmental impact of buildings. Building refurbishment is also seen as the most cost-effective way of achieving this goal. In assessing the life cycle impacts of constructing and usage processes of buildings, LCA (life cycle analysis) is often used. In order to simplify the decision-making process in early design, this study uses MOGA (multi objective genetic algorithms) to find optimal designs for a refurbishment of a residential complex case study, in terms of LCCF (life cycle carbon footprint) and LCC (life cycle cost) over an assumed life span of 60 years. Results show that utilizing MOGA has the potential to reduce the refurbishment LCCF and LCC. Findings emphasize the life-cycle impacts of insulating thermal bridges and the importance of using different heating systems and fuels. Finally, in comparing LCA with more commonly used performance-based decision-making design procedures, the study highlights that employing these distinctive methods can lead to different design solutions. Highlights: We use Multi Objective Genetic Algorithms to optimise refurbishment designs. We used Life Cycle Carbon Footprint and Life Cycle Cost as objective criteria. MOGA chose a design with the thickest insulation and smallest available windows. The utilization of MOGA has the potential to reduce LCCF and LCC. Thermal bridge insulation and different fuel types can impact LCCF and LCC.