2026-09-19T01:25:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/901362022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Steven B. Leeb and John S. Donnal.
Gillman, Mark Daniel
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-09-19T21:41:53Z
2014-09-19T21:41:53Z
2014
2014
http://hdl.handle.net/1721.1/90136
890151145
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
50
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 155-160).
Non-intrusive load monitoring (NILM) has three distinct advantages over today's smart meters. First, it offers accountability. Few people know where their kWh's are going. Second, it is a maintenance tool. Signs of wear are detectable through their electrical signal. Third, it provides awareness of human activity within a network. Each device has an electrical fingerprint, and specific devices imply associated human actions. From voltage and current measurements at a single point on the network, non-intrusive load monitoring (NILM) disaggregates appliance-level information. This information is available remotely in bandwidth-constrained environments. Four real-world field tests at military micro grids and commercial buildings demonstrate the utility of the NILM in reducing electrical demand, enabling condition-based maintenance, and inferring human activity from electrical activity.
by Mark Daniel Gillman.
S.M.
160 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Interpreting human activity from electrical consumption data through non-intrusive load monitoring
Thesis
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<Abstract>Non-intrusive load monitoring (NILM) has three distinct advantages over today's smart meters. First, it offers accountability. Few people know where their kWh's are going. Second, it is a maintenance tool. Signs of wear are detectable through their electrical signal. Third, it provides awareness of human activity within a network. Each device has an electrical fingerprint, and specific devices imply associated human actions. From voltage and current measurements at a single point on the network, non-intrusive load monitoring (NILM) disaggregates appliance-level information. This information is available remotely in bandwidth-constrained environments. Four real-world field tests at military micro grids and commercial buildings demonstrate the utility of the NILM in reducing electrical demand, enabling condition-based maintenance, and inferring human activity from electrical activity.</Abstract>
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