The best way to perform building simulations? One-at-a-time optimization vs. Monte Carlo sampling

What is the best approach to perform building simulations as means to support decision-making and optimize building performance? Traditionally, the search for a compliant design is carried out in a manual one-at-a-time (OAT) approach where one design parameter is optimized before attention is shifted to the next one. In contrast, the Monte Carlo method makes it possible to combine many design parameters simultaneously and consider numerous input combinations. This paper presents a comparison of design approaches and their ability to optimize the performance of an office room with respect to energy demand and indoor climate. The test case is an office space with eight discretized inputs resulting in 93,750 possible designs. Bootstrapping is used to vary the baseline and fixing order for two systematic OAT approaches and to assess an increasing number of Monte Carlo samples. Summarily, OAT optimization depends heavily on the baseline and reveals only few, local optima close to this baseline, which are potentially far from the best solutions. On average, sampling 32 random simulations unveils better solutions and is less likely to suggest poor solutions. An increased sample of 1024 simulations reveals diverse solutions and favourable input ranges, while enabling sensitivity analysis, metamodeling, and flexible constraints.