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dc.contributor.author
Tourpeslis, Iordanis
en
dc.date.accessioned
2022-07-05T10:03:35Z
dc.date.available
2022-07-05T10:03:35Z
dc.date.issued
2022-07-05
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29986
dc.rights
Default License
dc.subject
Data mining
el
dc.subject
Smart cities
el
dc.title
Data Mining for Smart Cities: Energy Disaggregation and Recommendation System
en
heal.type
masterThesis
en_US
heal.dateAvailable
2022-05-16
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
en_US
heal.publicationDate
2022-05-16
heal.abstract
One major factor that can influence sustainable living of people is the understanding of energy data consumption to achieve behavioral change which in further will lead to minimization of energy expenses as well as decreasing the waste footprint to the climate. Energy disaggregation tries to enhance the process of leaning the energy behavior of the user by categorizing the total consumption of a household into appliance level consumption. This study aims to develop an energy disaggregation system that will produce recommendations on which actions could be taken to minimize the energy consumption of the household based on the final output of the disaggregation process.
el
heal.advisorName
Tjortjis, Christos
el
heal.committeeMemberName
Bozanis, Panayiotis
en
heal.committeeMemberName
Stavrinides, Stavros
en
heal.academicPublisher
IHU
el
heal.academicPublisherID
ihu
en_US


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