Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Integration of Smart Home Technologies for District Heating Control in Pervasive Smart Grids
Malmö högskola, Faculty of Technology and Society (TS). Malmö högskola, Internet of Things and People (IOTAP).
Malmö högskola, Faculty of Technology and Society (TS). Malmö högskola, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0998-6585
2017 (English)In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2017, p. 515-520Conference paper, Published paper (Refereed)
Abstract [en]

Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study.

Place, publisher, year, edition, pages
IEEE, 2017. p. 515-520
Series
Proceedings of the ... IEEE International Conference on Pervasive Computing and Communications, ISSN 2474-2503
Keywords [en]
Computer Science, Information Systems, Computer Science, Theory & Methods, Telecommunications
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-12612DOI: 10.1109/PERCOMW.2017.7917616ISI: 000411208400104Scopus ID: 2-s2.0-85020011731Local ID: 27303OAI: oai:DiVA.org:mau-12612DiVA, id: diva2:1409659
Conference
IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA (13-17 March 2017)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2024-02-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Mihailescu, Radu-CasianDavidsson, Paul

Search in DiVA

By author/editor
Mihailescu, Radu-CasianDavidsson, Paul
By organisation
Faculty of Technology and Society (TS)Internet of Things and People (IOTAP)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 36 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf