A learning model of intelligent home

Abstract

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.

Citation

Yiu, C.Y. and Yau, Y. (2006), "A learning model of intelligent home", Facilities, Vol. 24 No. 9/10, pp. 365-375. https://doi.org/10.1108/02632770610677646