Occupancy Based Fault Detection on Building Level - a Feasibility Study

dc.creator	Tuip, B.
dc.creator	Houten, M.
dc.creator	Trcka, M.
dc.creator	Hensen, M.
dc.date.accessioned	2011-03-25T21:24:42Z
dc.date.available	2011-03-25T21:24:42Z
dc.date.issued	2010
dc.identifier.other	ESL-IC-10-10-36
dc.identifier.uri	https://hdl.handle.net/1969.1/94082
dc.description.abstract	Buildings rarely perform as designed. Improving building functioning could be of great value for different stakeholders as building users, building owners and maintenance companies. In this study, a prototype procedure is developed for an on-line, self learning fault detection tool on building level. Taking passive user behavior into account, the tool aims to distinguish real faults from unexpected user behavior. An artificial neural network model is used to predict building energy consumption based on real time weather conditions and occupancy. Fault detection is performed by comparing this predicted consumption with measured values. The prototype procedure is currently tested in an office building in the Netherlands, the first results are promising.	en
dc.publisher	Energy Systems Laboratory (http://esl.tamu.edu)
dc.publisher	Texas A&M University (http://www.tamu.edu)
dc.subject	Buildings	en
dc.subject	Fault Detection Tool	en
dc.subject	Building Energy Consumption	en
dc.title	Occupancy Based Fault Detection on Building Level - a Feasibility Study	en
dc.contributor.sponsor	Unit Building Performance and Systems. Eindhoven University of Technology, Netherlands