Title: Home appliances classification based on multi-feature using ELM

Authors: Qi Liu; Fangpeng Chen; Fenghua Chen; Zhengyang Wu; Xiaodong Liu; Nigel Linge

Addresses: Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology (CICAEET), School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science and Technology, Nanjing, 210044, China ' South China Normal University, College of Computer, Guangzhou, China ' School of Computing, Edinburgh Napier University, 10 Clinton Road, Edinburgh EH10 5DT, UK ' School of Computing, Science and Engineering, The University of Salford, Salford, Greater Manchester, M5 4WT, UK

Abstract: With the development of science and technology, smart home has become a hot topic. And pattern recognition adapting to smart home attracts more attention, while the improvement of the accuracy of recognition is an important and difficult issue of smart home. In this paper, the characteristics of electrical appliances are extracted from the load curve of household appliances, and a fast and efficient home appliance recognition algorithm is proposed based on the advantage of classification of extreme learning machine (ELM). At the same time, the sampling frequency with low rate is mentioned in this pa-per, which can obtain the required data through intelligent hardware directly, as well as reduce the cost of investment. Experiments in this paper show that the proposed method can accurately determine the using electrical appliances. And greatly improve the accuracy of identification, which can further improve the popularity of smart home.

Keywords: feature extraction; smart home; data collection; WSN; wireless sensor network; ELM; extreme learning machine; smart socket; data analysis.

DOI: 10.1504/IJSNET.2018.094710

International Journal of Sensor Networks, 2018 Vol.28 No.1, pp.34 - 42

Received: 28 Sep 2017
Accepted: 22 Nov 2017

Published online: 08 Sep 2018 *

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