Masters Theses
Abstract
"This work presents an appliance disaggregation technique to handle the fundamental goal of the Non-Intrusive Appliance Load Monitoring (NIALM) problem i.e., a simple breakdown of an appliance level energy consumption of a house. It also presents the modeling of individual appliances as load models using hidden Markov models and combined appliances as a single load model using factorial hidden Markov models. Granularity of the power readings of the disaggregated appliances matches with that of the readings collected at the service entrance. Accuracy of the proposed algorithm is evaluated using publicly released Tracebase data sets and UK-DALE data sets at various sampling intervals. The proposed algorithm achieved a success rate of 95% and above with Tracebase data sets at 5 second sampling resolution and 85% and above with UK-DALE data sets at 6 second sampling resolution"--Abstract, page iii.
Advisor(s)
McMillin, Bruce M.
Committee Member(s)
Kimball, Jonathan W.
Chellappan, Sriram
Department(s)
Computer Science
Degree Name
M.S. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2015
Pagination
vii, 34 pages
Note about bibliography
Includes bibliographical references (pages 32-33).
Rights
© 2015 Anusha Sankara, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Household appliances -- Energy consumption -- Computer simulation
Hidden Markov models
Energy consumption -- Measurement
Thesis Number
T 10692
Electronic OCLC #
913514687
Link to Catalog Record
Recommended Citation
Sankara, Anusha, "Energy disaggregation in NIALM using hidden Markov models" (2015). Masters Theses. 7414.
https://scholarsmine.mst.edu/masters_theses/7414