تنوع راه حل های بهینه برای اجزای ساختمان بر اساس تجزیه و تحلیل جامع چرخه هزینه عمر

Building energy contributes to a significant fraction of the total energy cost during all processes of its life span. Buildings are dynamic, non-linear systems with a large number of components that strongly influence total building energy consumption. Therefore, it is challenging to find an optimal combination of building components to minimize the building life cycle cost (LCC). This paper proposes a framework of building systems optimization designed to minimize life cycle cost by combining optimization algorithms and a comprehensive building life cycle cost model. A case study based on an office building demonstrates that annual energy costs and initial construction costs are major contributors to the whole building life cycle cost. A case study of an office building shows that when the building lifespan is greater than 30 years, the cumulative annual energy consumption cost is projected to be higher than the initial construction cost. Finally, optimal component combinations vary with different lengths of a building's life span. For instance, wood window frames become the optimal component for less energy and maintenance cost when the building lifespan changes from 14 to 60 years.

Building energy contributes to a significant fraction of the total national energy cost during a building's lifetime. In developed countries, building energy consumption represents 20–40% of total national energy use and this percentage is above the industry and transportation figures in EU and the US. The growing trend in building energy consumption will continue during the coming years due to building floor area expansion and associated energy needs [1]. Buildings have relatively long life spans. In the U.S., the median lifetime of commercial buildings is 50–65 years [2]. In addition, buildings are dynamic, non-linear systems with a large number of components that strongly affect building energy consumption. This complicates optimizing total building energy consumption. Several specific components that contribute strongly to total building life cycle cost (LCC), most notably building enclosure and mechanical systems [3]. Life cycle assessment (LCA) is a methodological framework for estimating and assessing the environmental impacts attributable to the lifecycle of a product [4]. Following the definition and guidance in the International Organization for Standardization (ISO) 14040 [5], building life cycle includes construction, annual operation and maintenance, and demolition. The cash flow during these phases contains construction capital cost, transportation cost, annual operating energy consumption, maintenance cost, and demolition cost.

This study determines a framework to optimize building components using genetic algorithms with the objective of optimizing building life cycle cost. Different from previous studies, the life cycle analysis methodology proposed here covers all phases of the building life cycle. The annual energy consumption – an important aspect of life cycle cost – is calculated by an energy simulation tool instead of referring to a fixed database. The case study based on a medium size office building demonstrates the proposed life cycle optimization framework. The results show that the capital cost and operating energy consumption are both major parts of life cycle cost. The annual energy consumption becomes more important when the building has a longer life span. In this case study, the operating energy cost takes the largest percentage of the life cycle cost when the building has more than 30 years life span. It also demonstrates that the optimal building components are very different according to different objectives. Optimal combination for annual energy cost highlights the material with best thermal properties, while cheap materials and smaller HVAC system size are emphasized in optimization of capital cost. Therefore, a comprehensive life cycle analysis model provides a better understanding of objectives for building systems optimization. It also suggests that combinations of optimized building components are dependent on the building life span. There are two energy pricing scenarios considered in this study. The results indicate that the increasing prices will influence life cycle cost and optimization results.