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

TLDR

IoT enables easy deployment of smart homes, allowing residents to control, monitor, and manage energy consumption without wastage. This paper introduces a self‑learning home management system. The system integrates home, demand‑side, and supply‑side management modules with price forecasting, clustering, and power‑alert capabilities, built using computational and machine‑learning techniques and validated with real‑time data from a Singapore smart home. A case study demonstrated that the system performs well, raises residents’ energy awareness, and can be customized for various environments beyond traditional smart‑home models.

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.