Fault Detection using KPCA analysis applied to building efficiency

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 ene...

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Main Author: petit-pierre, melec
Format: Others
Language:English
Published: KTH, Tillämpad termodynamik och kylteknik 2015
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166733
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spelling ndltd-UPSALLA1-oai-DiVA.org-kth-1667332015-08-28T04:57:09ZFault Detection using KPCA analysis applied to building efficiencyengpetit-pierre, melecKTH, Tillämpad termodynamik och kylteknik2015Over 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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166733application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
description 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.
author petit-pierre, melec
spellingShingle petit-pierre, melec
Fault Detection using KPCA analysis applied to building efficiency
author_facet petit-pierre, melec
author_sort petit-pierre, melec
title Fault Detection using KPCA analysis applied to building efficiency
title_short Fault Detection using KPCA analysis applied to building efficiency
title_full Fault Detection using KPCA analysis applied to building efficiency
title_fullStr Fault Detection using KPCA analysis applied to building efficiency
title_full_unstemmed Fault Detection using KPCA analysis applied to building efficiency
title_sort fault detection using kpca analysis applied to building efficiency
publisher KTH, Tillämpad termodynamik och kylteknik
publishDate 2015
url http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166733
work_keys_str_mv AT petitpierremelec faultdetectionusingkpcaanalysisappliedtobuildingefficiency
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