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For reliable building performance assessment, uncertainty caused by future climate change and future energy retrofit should be taken into account. This paper presents a case study of risk-based decision making driven by future uncertainty sources (climate, energy retrofit) in an existing office building. For the study, the EnergyPlus model was chosen and a Monte Carlo Sampling (MCS) technique was employed. Then, significant inputs were identified using a global sensitivity analysis. This study compares three alternatives for replacement of a chiller using deterministic and risk-based stochastic approaches. It is shown that the risk-based stochastic approach is more useful than the deterministic approach. In particular, it is highlighted that the uncertainty sources (climate, retrofit, etc.) have an important bearing on the selection of optimal alternatives.