The current methods of building energy simulation that designers & engineers (D&E) use in order to find the energy performance of a building do not take into account the real behavior of the people who will use the building. The main aim of this paper is to show how by merely including the real behavior of people in building simulations there may be differences of up to 30%, through the study of a real pilot site simulation with existing software. These data confirm the possibilities of energy and money saving that energy simulation programs bring about when they include schedules of true use of the building (BIM).

The current methods of building energy simulation that designers and engineers (D&E) use in order to find the energy performance of a building do not take into account the real behavior and daily activities of the people who will use the building. The main aim of this paper is to demonstrate that a system for building simulation, that produces data about the activity behaviour of occupants as members of an enterprise structure and framework, can significantly improve the relevance and performance of building simulation tools, through the study of a real building in daily operation. Furthermore, data (BIM, BPM and occupancy data) has been performed exploiting Open Reference Data Modelling methodology in order to be reusable.

The article presents the process of placing sensors in a multi-sensorial network, dynamically incorporating a large number of heterogeneous input sources able to provide accurate monitoring data related with space occupancy, energy consumption, comfort levels and environmental quality.To evaluate this multi-sensorial network on real life conditions and on the specific business domains addressed by the Project, this sensing network will be based on heterogeneous sensors (light, motion, CO2, CO, temperature, relative humidity, existing infrastructure on video-surveillance, depth/range image generators, energy consumption, etc.) in order to provide an all-inclusive perspective of covered spaces. Thearticleispart of a global projecttodevelopprivacy-preserving human detection and tracking toolkit, whith the implementation of algorithms for calibration of multiple-depth sensors in the architectural sketch up of a building (BIM), and the development of techniques for extraction of occupancy-related statistics in the spatio-temporal domain of a building. It is an architectural prototype agile and scalable, integrated with the extended LS middleware, quepermite the training and calibration as decision making toolkit for Facility Managers.