A Smart IoT based Non-Intrusive Appliances Identification Technique in a Residential System

To improve the living standard of smart cities, remote monitoring of residential appliances is a significant aspect for home energy management system. With enormous evolution of the internet and machine intelligence, smart monitoring of home appliances can be realized using the Internet of Things (IoT) platform. In this paper, few statistical features extracted from the raw time series electrical signal is used to classify or identify the appliances. Using these statistical features, an IoT based fuzzy rule technique is built to monitor the residential appliances. The IoT based approach for monitoring the electrical appliances is done such that reducing time and resources can be achieved more proficiently. The IoT based platform, allows to identify appliances remotely using present network structure. It is also making prospects of more economical, higher efficiency with least human interference. Furthermore, a prototype system in laboratory has been demonstrated and tested for viability of the proposed load monitoring technique.

[1]  Md. Sanwar Hossain,et al.  A smart IoT based system for monitoring and controlling the sub-station equipment , 2019, Internet Things.

[2]  Amit Konar,et al.  Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain , 1999 .

[3]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[4]  Masahiro Inoue,et al.  Integrated residential gateway controller for home energy management system , 2003, IEEE Trans. Consumer Electron..

[5]  Christos V. Verikoukis,et al.  A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms , 2015, IEEE Communications Surveys & Tutorials.

[6]  Saifur Rahman,et al.  An Algorithm for Intelligent Home Energy Management and Demand Response Analysis , 2012, IEEE Transactions on Smart Grid.

[7]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[8]  Yoash Levron,et al.  MO-NILM: A multi-objective evolutionary algorithm for NILM classification , 2019, Energy and Buildings.

[9]  Saifur Rahman,et al.  Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities , 2014, IEEE Transactions on Smart Grid.

[10]  Arunava Chatterjee,et al.  Load monitoring of residential elecrical loads based on switching transient analysis , 2017, 2017 IEEE Calcutta Conference (CALCON).

[11]  Arunava Chatterjee,et al.  Improved non-intrusive identification technique of electrical appliances for a smart residential system , 2019 .

[12]  Pradeep Kumar Juneja,et al.  Identifying Appliances using NIALM with Minimum Features , 2014 .

[13]  Somchai Thepphaeng,et al.  Implementation of WiFi-based single phase smart meter for Internet of Things (IoT) , 2017, 2017 International Electrical Engineering Congress (iEECON).

[14]  S. Bacha,et al.  Appliance usage prediction using a time series based classification approach , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[15]  Walid G. Morsi,et al.  A novel feature extraction and classification algorithm based on power components using single-point monitoring for NILM , 2015, 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE).