Sensor Placement for Bpm Analysis of Buildingsin Use to Implement Energy Savings Through Building Performance Simulation
Abstract
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.
Full Text: PDF DOI: 10.15640/jea.v2n2a10
Abstract
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.
Full Text: PDF DOI: 10.15640/jea.v2n2a10
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