Building simulations supporting decision making in early design – A review. (August 2016)

• Record Type: Journal Article
• Authors: Østergård, Torben; Jensen, Rasmus L.; Maagaard, Steffen E.
• Is Part Of: Renewable & sustainable energy reviews. Volume 61(2016:Aug.)
• Page Start: 187
• Page End: 201
• Publication Date: 2016-08
• Subjects: BPS Building performance simulation -- OAT one-at-a-time -- UA uncertainty analysis -- SA sensitivity analysis -- LCC life cycle costs -- LCA life cycle analysis; Building performance -- Uncertainty analysis -- Sensitivity analysis -- Interoperability -- Optimisation -- Knowledge based input generation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.03.045
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The building design community is challenged by continuously increasing energy demands, which are often combined with ambitious goals for indoor environment, for environmental impact, and for building costs. To aid decision-making, building simulation is widely used in the late design stages, but its application is still limited in the early stages in which design decisions have a major impact on final building performance and costs. The early integration of simulation software faces several challenges, which include time-consuming modeling, rapid change of the design, conflicting requirements, input uncertainties, and large design variability. In addition, building design is a multi-collaborator discipline, where design decisions are influenced by architects, engineers, contractors, and building owners. This review covers developments in both academia and in commercial software industry that target these challenges. Identified research areas include statistical methods, optimisation, proactive simulations, knowledge based input generation, and interoperability between CAD-software and building performance software. Based on promising developments in literature, we propose a simulation framework that facilitates proactive, intelligent, and experience based building simulation which aid decision making in early design. To find software candidates accommodating this framework, we compare existing software with regard to intended usage, interoperability, complexity, objectives, and ability to perform various parametric simulations.