Statistical Decision Assistance for Determining Energy-Efficient Options in Building Design under Uncertainty.

Designers need to compare numerous design options in the process of designing an energy-efficient building. There are two impediments in this process, first probabilistic prediction of energy requirement at an early stage of design with uncertain design parameters and, second, selecting a design option based on the probabilistic energy prediction. The paper presents an integration of machine learning energy prediction model with building information modelling (BIM) tool to make probabilistic energy prediction, i.e. ranges of values. Wilcoxon rank-sum test is useful in this situation, which is capable of comparing alternatives based on probabilistic energy predictions. The tool has been developed to extract information from the BIM model, make probabilistic energy prediction using the Monte Carlo method, and perform statistical analysis. It has been found that BIM integrated machine learning model can make energy prediction of six design alternatives in 30-35 seconds with no additional modelling efforts. Higher uncertainty in the design parameters will result in larger uncertainty in the energy prediction, and the test may not be able to suggest the better option even using statistical comparison. This will require the more precise value of design parameters, i.e. reduced uncertainty. Different uncertainty levels in the design parameters have been tested to which extent they are sufficient to make a selection of the energyefficient option. It is observed that uncertainty levels that are suitable for decision-making depend on the combination of design options to be compared. It is possible to differentiate among alternatives with high uncertainty in the design parameters if they are entirely different else more precise definition of the design parameters is required. This research provides a method to select a better option among the developed options based on energy performance at the early stage of design.