Integrated design tool of building system optimization for building life cycle cost
The optimization of energy efficient buildings is a highly complex problem and requires a long running process due to the many options that exist at the time that a building is being designed. Although this is the time when critical decisions can be made that have the largest impact on building life-cycle cost (LCC), such large-scale optimization problems are often prohibitive within the building industry because of the excessive computational time. Therefore, this research aims to develop an accurate and efficient integrated design tool for performing building life cycle cost optimization. The developed methodology includes follow objectives. (1) Develop a detailed building life cycle cost analysis. To predict energy consumption accurately, detailed modeling is done with energy simulation software. To evaluate construction cost, realistic data is taken from actual construction and equipment cost database. (2) Using the variable selection process, significant variables that demonstrate the most significant contribution in the optimization study are identified. By identifying these variables the design space is reduced significantly. (3) To overcome the long computational time required to generate sufficient data that can be used during the variable selection process, a simplified energy consumption model is developed to replace the full annual energy simulation. (4) With an appropriately reduced number of input design variables the optimization methodology is applied to building life cycle cost using energy simulation software and available cost data. (5) With optimized result of the significant variables, the design space is explored near optimum to identify the best value of the insignificant variables to get closer to the true optimum. The developed methodology has been applied to three residential building types; a residential building with crawl space, a residential building with slab on grade, and a residential building with a heated basement in multiple locations in the United States. The case study results show that the developed methodology effectively and accurately finds the optimum point compared to the full optimization process with all design variables.
Horton, Purdue University.
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