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 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.

[1]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[2]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[3]  Chris D. Nugent,et al.  From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes , 2017, IEEE Transactions on Human-Machine Systems.

[4]  Mohammad Abdullah Al Faruque,et al.  Energy Management-as-a-Service Over Fog Computing Platform , 2015, IEEE Internet of Things Journal.

[5]  Diane J. Cook,et al.  The role of prediction algorithms in the MavHome smart home architecture , 2002, IEEE Wirel. Commun..

[6]  Ai-Chun Pang,et al.  Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids , 2017, IEEE Internet of Things Journal.

[7]  François Carrez,et al.  A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things , 2015, IEEE Internet of Things Journal.

[8]  Fredrik Wallin,et al.  A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks , 2016, IEEE Internet of Things Journal.

[9]  John R. Williams,et al.  P3: Privacy Preservation Protocol for Automatic Appliance Control Application in Smart Grid , 2014, IEEE Internet of Things Journal.

[10]  Ren C. Luo,et al.  Multisensor fusion and integration: approaches, applications, and future research directions , 2002 .

[11]  Scott Dick,et al.  Toward Non-Intrusive Load Monitoring via Multi-Label Classification , 2017, IEEE Transactions on Smart Grid.

[12]  Il-Woo Lee,et al.  Smart home energy management system including renewable energy based on ZigBee and PLC , 2014, IEEE Transactions on Consumer Electronics.

[13]  Steven B. Leeb,et al.  Energy Applications for an Energy Box , 2016, IEEE Internet of Things Journal.

[14]  Debashis De,et al.  Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network , 2016, IET Wirel. Sens. Syst..

[15]  Abbas Javed,et al.  Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC , 2017, IEEE Internet of Things Journal.

[16]  Raj Jain,et al.  An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments , 2015, IEEE Internet of Things Journal.

[17]  Alagan Anpalagan,et al.  Efficient Energy Management for the Internet of Things in Smart Cities , 2017, IEEE Communications Magazine.

[18]  Thillainathan Logenthiran,et al.  Housing Development Building Management System (HDBMS) For Optimized Electricity Bills , 2017 .

[19]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[20]  Shiwen Mao,et al.  Distributed Online Algorithm for Optimal Real-Time Energy Distribution in the Smart Grid , 2013, IEEE Internet of Things Journal.

[21]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[22]  Abdelsalam Helal,et al.  Scalable Cloud–Sensor Architecture for the Internet of Things , 2016, IEEE Internet of Things Journal.

[23]  Jun Jason Zhang,et al.  Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods , 2014, IEEE Transactions on Smart Grid.

[24]  Junhong Xu,et al.  Survey on Prediction Algorithms in Smart Homes , 2017, IEEE Internet of Things Journal.

[25]  T. Logenthiran,et al.  Intelligent multi-agent system for smart home energy management , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[26]  Thillainathan Logenthiran,et al.  Intelligent Multi-Agent System for Power Grid Communication , 2016, 2016 IEEE Region 10 Conference (TENCON).

[27]  Kazem Sohraby,et al.  IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems , 2017, IEEE Internet of Things Journal.

[28]  Biplab Sikdar,et al.  A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications , 2015, IEEE Internet of Things Journal.

[29]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[30]  David Irwin,et al.  Pervasive Energy Monitoring and Control Through Low-Bandwidth Power Line Communication , 2017, IEEE Internet of Things Journal.

[31]  Thillainathan Logenthiran,et al.  Implementation of demand side management of a smart home using multi-agent system , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).