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IDENTIFYING UNUSUAL ENERGY CONSUMPTIONS OF HOUSEHOLDS: Using Inductive Conformal Anomaly Detection approach
Havugimana, Léonce
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2020 (English)
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Place, publisher, year, edition, pages
2020.
Keywords [en]
Inductive Conformal Anomaly Detection, Energy Consumption, Sub-sequence, Non-Conformity Measure
National Category
Computer Sciences
Identifiers
URN:
urn:nbn:se:bth-20220
OAI: oai:DiVA.org:bth-20220
DiVA, id:
diva2:1454326
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVACS Master of Science Programme in Computer Science
Supervisors
Anton, Borg, PhD
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Christian, Nordahl
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Examiners
Emilia, Mendes, Professor
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Available from:
2020-08-03
Created:
2020-07-15
Last updated:
2020-08-03
Bibliographically approved
Open Access in DiVA
IDENTIFYING UNUSUAL ENERGY CONSUMPTIONS OF HOUSEHOLDS: Using Inductive Conformal Anomaly Detection approach
(810 kB)
300 downloads
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FULLTEXT02.pdf
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effacd9ea11608b656b3a4780eaa082e303c265aee93c55dea9bfb2ccf91e5050336042214a9e3f5fbaa9ebb634f9591913194dd60b4fe0a151b0427ce81a376
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Total: 300 downloads
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modern-language-association-8th-edition
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ieee
modern-language-association-8th-edition
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