Implemented IoT-Based Self-Learning Home Management System (SHMS) for Singapore

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 sys...

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Bibliographic Details
Published in:IEEE internet of things journal 2018-06, Vol.5 (3), p.2212-2219
Main Authors: Weixian Li, Logenthiran, Thillainathan, Van-Tung Phan, Wai Lok Woo
Format: Article
Language:English
Subjects:
Case studies
Clustering
Clustering algorithms
Demand side management
Energy consumption
Energy management
Internet of Things
Internet of Things (IoT)
Logic gates
Machine learning
Mathematical model
Power consumption
Real time operation
Residential energy
self-learning home management system (SHMS)
Smart homes
Smart houses
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Summary: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.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2018.2828144