Implemented IoT-based self-learning home management system (SHMS) for Singapore

Weixian Li, Thillainathan Logenthiran, Van Tung Phan, Wai Lok Woo

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

Internet of Things makes deployment of smart home concept easy and real. Smart home concept ensures residents to control, monitor, and manage their energy consumption without any wastage. This paper presents a self-learning home management system. In the proposed system, a home energy management system, demand side management system, and supply side management system were developed and integrated for real time operation of a smart home. This integrated system has some capabilities such as price forecasting, price clustering, and power alert system to enhance its functions. These enhancing capabilities were developed and implemented using computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data was collected from a Singapore smart home and a realistic experimental case study was carried out. The case study has shown that the developed system has performed well and created energy awareness to the residents. This proposed system also displays its ability to customize the model for different types of environments compared to traditional smart home models.
Original languageEnglish
Pages (from-to)2212-2219
Number of pages8
JournalIEEE Internet of Things Journal
Volume5
Issue number3
Early online date18 Apr 2018
DOIs
Publication statusPublished - 8 Jun 2018

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