A learning model of intelligent home

Chung Yim YIU*, Yung YAU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


Purpose - This paper aims to review the extant intelligent home specifications and put forward a new dimension for the specifications of intelligent home (IHS). 

Design/methodology/approach - This study adopts a learning (bottom-up) algorithm which emphasizes the importance of learning and adaptability to the dynamic environmental changes in the IHS. 

Findings - The study finds that the intelligent home has been characterized by automation, integration of facilities and communication. However, it is contended here that an intelligent home specification in such a hard-wired (top-down) approach cannot be sustained in the light of the continuous changes of user requirements. Hence, adaptation to users' needs must be encompassed in a system of home intelligence. 

Research limitations/implications - This study provides a framework for all stakeholders to work for a common goal and a platform for benchmarking the performance of intelligent home in the long run. 

Originality/value - This is the first to adopt the learning (bottom-up) algorithm in defining home intelligence.

Original languageEnglish
Pages (from-to)365-375
Number of pages11
Issue number9-10
Publication statusPublished - 15 Jul 2006
Externally publishedYes


  • Automation
  • Buildings
  • Learning


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