Fault Detection using KPCA analysis applied to building efficiency
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Over the past few years, due to global warming and the depletion of energy resources, the reduction of buildings energy consumption and control systems in buildings have received more attention than ever. In particular, the management of faults aﬀecting buildings is a ﬁeld that could enable huge energy savings. This report investigates the coupling of a statistical analysis with a building simulation software with the aim of detecting faults. To do so the data collected from the buildings is compared to the simulation results of the model using IDA ICE. Then a Fault Detection algorithm, that has been developed based on the Kernel Principal Component analysis, processes those diﬀerences complemented by several variables that help the algorithm to understand the behavior of the building. Since data with identiﬁed faults was not available, this methodology has been tested on simulation versus simulation comparison based on the model of a well known building. Several faults were then simulated and were successfully detected by the algorithm.
Place, publisher, year, edition, pages
2015. , 51 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-166733OAI: oai:DiVA.org:kth-166733DiVA: diva2:812024
Master of Science - Sustainable Energy Engineering
Madani, Hatef, doctor