Abstract
Building Information Modeling (BIM) is a critical element for the “digitalization” of the construction industry and can be exploited for energy-driven renovation procedures of existing residences. Advancing beyond a BIM with data-capturing capabilities that are limited to building static information only requires sensor data streams related to indoor/outdoor ambient conditions, as well as to energy-consumption parameters of the residences. The data streams require the deployment of robust Wireless Sensor Networks (WSNs) that are able to capture and transmit real-time data to appropriate cloud-based renovation toolkits. The technology and topology of such networks are addressed herein. The paper sets the lines for similar installations that are required by the construction industry for collecting dynamic data, since it is based on the outcome of real-world WSN installations in pilot sites in three European countries, carried out in the context of a major collaborative BIM research project. An application example of the WSN data is also provided in the context of training occupant behavior models in order to demonstrate the use of the measured data.
Highlights
Athanasiadou, G.; Giannakis, G.BIMERR (BIM-based holistic tools for Energy-driven Renovation of existing Residences) is a research and innovation program which started in 2018, supported by a consortium of 16 members, including EU companies and Universities, and co-funded by the European Union Horizon 2020 [1]
This paper addresses the deployment, technology, topology and cost of robust Wireless Sensor Networks (WSNs) that are able to collect and transmit real-time data required from Building Information Modeling (BIM) renovation toolkits
It is based on real WSN installations, rather than simulated results, as in most cases reported in the open literature
Summary
Applying machine-learning algorithms on the sensor-data streams provided by the WSNs that are designed for and installed in the pilot sites generates occupant-behavior profiles that mimic the inhabitants’ actions and contribute to the accuracy of energy performance estimation, using ML-enabled occupant behavior methods [23,24,25,26] These profiles are subsequently used to project the building system (e.g., heating/cooling) utilization boundaries to maintain the comfort zone of the residents. It is based on real WSN installations, rather than simulated results, as in most cases reported in the open literature As a consequence, it considers different field requirements, constraints and options at the BIMERR pilot sites, and, it sets the lines for similar installations required by the construction industry for collecting dynamic data required for BIM and smart buildings.
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