Temporal Mining Approaches for Smart Buildings Research

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dc.contributor.authorShao, Huijuanen
dc.contributor.committeechairRamakrishnan, Narenen
dc.contributor.committeememberLu, Chang-Tienen
dc.contributor.committeememberVullikanti, Anil Kumar S.en
dc.contributor.committeememberMarwah, Manishen
dc.contributor.committeememberPrakash, B. Adityaen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-07-25T06:00:21Zen
dc.date.available2018-07-25T06:00:21Zen
dc.date.issued2017-01-30en
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
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:8974en
dc.identifier.urihttp://hdl.handle.net/10919/84349en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectData miningen
dc.subjectSustainabilityen
dc.subjectEnergy disaggregationen
dc.subjectOccupancy predictionen
dc.titleTemporal Mining Approaches for Smart Buildings Researchen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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