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Nowadays, building energy models use parametric analyses to optimize design strategies considering multiple variables. Integrated dynamic models combining design tool and visual programming language (VPL) and simulation tools to calculate building performance with BIM tool for the whole-building energy simulation have been adopted in the recent studies. Through these tools, it is possible to identify parametric systems, which become a “genome”, where a rapid comparison of different alternatives is possible through fitness criteria defined by design goals. The aim of the paper is to use this concept and the suitable parametric tools such as Grasshopper for Rhinoceros to handle variable hypotheses on users’ occupancy that influence building energy performance. The paper focuses on occupancy variability applying the methodology to a university building located in northern Italy in the University of Brescia Campus to evaluate how generative modelling can represent an adequate approach to energy simulation of occupant behaviour. Sensors are now monitoring the real occupancy trend of the case study building and different scenarios defined in the parametric model could be compared to the real weekly. Using parametric tool and GA (Genetic Algorithms) can be analysed hundreds of occupancy patterns in order to better understand the influence of the occupancy on the building energy use and at the same time evaluate different strategies to save energy.

Occupancy Profile Variation Analyzed through Generative Modelling to Control Building Energy Behavior