Mining Device Data to Auto-commission Buildings: Poster Abstract

Publication Type Conference Paper
Authors Luis Ignacio Lopera Gonzalez, Oliver Amft
Title Mining Device Data to Auto-commission Buildings: Poster Abstract
Abstract Ideally, building devices should be installed in a set-and-forget manner with minimum device configuration. In practice however, the correct interaction between all devices must be assured in order for the building to function properly, e.g., each motion detector has to be associated to the correct ceiling lamps. Currently, the initial commissioning of the devices in a building is a manual task, labour intensive and prone to errors. In this work, we show how to use state of the art building management systems (BMS) and modern device design in order to minimize building commissioning time. We summarize device data mining techniques which extract device groups, the building hierarchical structure, and the relative position of spatial sensors; we explore their applicability to the building's commissioning and maintenance stages. Finally, we propose a technique to auto-commission the lighting and HVAC systems of buildings.
Date November 2016
Proceedings Title Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys '16)
Place New York, NY, USA
Publisher ACM
Pages 249–250
Series BuildSys '16
DOI 10.1145/2993422.2996413
ISBN 978-1-4503-4264-3
Short Title Mining Device Data to Auto-commission Buildings
URL Publisher's website
Accessed 2016-11-25T13:51:37Z
Library Catalog ACM Digital Library
Full Text PDF
Friedrich-Alexander-Universität Erlangen-Nürnberg