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Occupancy Based Fault Detection on Building Level - a Feasibility Study
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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.
Tuip, B.; Houten, M.; Trcka, M.; Hensen, M. (2010). Occupancy Based Fault Detection on Building Level - a Feasibility Study. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from