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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorVreeswijk, G.A.W.
dc.contributor.authorJansze, J.
dc.date.accessioned2013-07-08T17:01:23Z
dc.date.available2013-07-08
dc.date.available2013-07-08T17:01:23Z
dc.date.issued2013
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/13213
dc.description.abstractThis thesis is part of the SEAM4US project, which goal is to minimize the energy consumption of the Barcelona metro station. The energy minimization is done by so-called model predictive control, i.e. management of the energy using systems based on a time series prediction. Here we focus on such a prediction. We make use of the popular Fourier transformation in combination with trend detection and validate our method by testing on different data sets and comparing with well-known techniques. Furthermore we conclude that this technique is very usable for the SEAM4US project and probably for a lot of time series prediction.
dc.description.sponsorshipUtrecht University
dc.format.extent1429731 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleTime Series Machine Learning Technique with Application to Barcelona Metro Station Energy Minimization
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordstime series, Fourier, trend analysis, model predictive control
dc.subject.courseuuTechnical Artificial Intelligence


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