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Improving the Effectiveness of Building Automation by adaption to the Users Context
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. PERCCOM.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

The operations of either residential housing or commercial buildings are energy intensive, estimated to occupy around 40% of all energy consumed worldwide by the year 2030 (by GeSI, SMARTer2030). ICT-enabled smart home or building solutions are expected to contribute to sustainability gain in term of improving energy and resource efficiency. These technologies not only enable buildings to be automated and centrally controlled but also help to provide a healthier and more comfortable living or working environment. While studies in smart home system show good results in reducing the energy consumption of a building by automating tasks to tear down unused appliances, most of the applications are limited implemented based on fixed schedule reassembling user behavior or routines, which is one of the major obstacles for home automation systems (HAS) to be widely acquired. As a solution for this matter, this study aims at exploring actual contexts of user for HAS to adapt in a more meaningful way so that not only the goal of reduced energy consumption is improved, but the user comfort is also taken care of in the best way. Using available studies on the expected reaction in HAS (in this work we focus on German Use case), a rule-based dictionary will be defined as a set of meaningful adaptions which can later be implemented on top of a home automation platform. Then, the study will present the assessment of this model in comparison with available studies to prove an improvement for energy efficiency.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Home Automation, User Context, Effectiveness Improvement, Sustainable Development
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-76030OAI: oai:DiVA.org:ltu-76030DiVA, id: diva2:1351842
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level (120 credits)
Presentation
2019-06-17, Arenan theatre, Lulea University of Technology - Skelleftea Campus, Skellefteå, 13:30 (English)
Supervisors
Examiners
Available from: 2019-09-18 Created: 2019-09-17 Last updated: 2019-09-18Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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  • de-DE
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  • Other locale
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Output format
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