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dc.contributor.authorShao, Huijuanen_US
dc.date.accessioned2018-07-25T06:00:21Z
dc.date.available2018-07-25T06:00:21Z
dc.date.issued2017-01-30
dc.identifier.othervt_gsexam:8974en_US
dc.identifier.urihttp://hdl.handle.net/10919/84349
dc.description.abstractWith the advent of modern sensor technologies, significant opportunities have opened up to help conserve energy in residential and commercial buildings. Moreover, the rapid urbanization we are witnessing requires optimized energy distribution. This dissertation focuses on two sub-problems in improving energy conservation; energy disaggregation and occupancy prediction. Energy disaggregation attempts to separate the energy usage of each circuit or each electric device in a building using only aggregate electricity usage information from the meter for the whole house. The second problem of occupancy prediction can be accomplished using non-invasive indoor activity tracking to predict the locations of people inside a building. We cast both problems as temporal mining problems. We exploit motif mining with constraints to distinguish devices with multiple states, which helps tackle the energy disaggregation problem. Our results reveal that motif mining is adept at distinguishing devices with multiple power levels and at disentangling the combinatorial operation of devices. For the second problem we propose time-gap constrained episode mining to detect activity patterns followed by the use of a mixture of episode generating HMM (EGH) models to predict home occupancy. Finally, we demonstrate that the mixture EGH model can also help predict the location of a person to address non-invasive indoor activities tracking.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectData miningen_US
dc.subjectSustainabilityen_US
dc.subjectEnergy disaggregationen_US
dc.subjectOccupancy predictionen_US
dc.titleTemporal Mining Approaches for Smart Buildings Researchen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairRamakrishnan, Narendranen_US
dc.contributor.committeememberLu, Chang Tienen_US
dc.contributor.committeememberVullikanti, Anil Kumar S.en_US
dc.contributor.committeememberMarwah, Manishen_US
dc.contributor.committeememberPrakash, Bodicherla Adityaen_US


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