@inproceedings{e3cbe16189064796bea036d60f0b00a5,
title = "Home-legacy device intelligent control using ANFIS with data regeneration and resampling",
abstract = "In recent years, the research for electric power usage reduction in a house has been widely studied. Home energy management system (HEMS) is become one of major applications to handle many smart electrical devices inside house that proves its electricity usage reduction efficiently. However, HEMS has a critical issue which cannot control non-smart devices at home. Saving unnecessary energy usage for legacy devices remains research area to prevent from expansions of energy waste for users. In this paper, an intelligence inference control approach based on the adaptive neural-fuzzy inference system (ANFIS) is proposed for legacy devices. The approach based on ANFIS focuses to reduce computation of training time by performing regeneration and resampling approach compared to conventional ANFIS.",
keywords = "Artificial neural fuzzy inference system (ANFIS), Home energy management system (HEMS), Legacy device, Regeneration, Resampling",
author = "Junho Chung and Choi, {In Hwan} and Yoo, {Sung Hyun} and Lim, {Myo Taeg} and Lee, {Hyun Kook} and Song, {Moon Kyu} and Ahn, {Choon Ki}",
note = "Publisher Copyright: {\textcopyright} 2015 Institute of Control, Robotics and Systems - ICROS.; 15th International Conference on Control, Automation and Systems, ICCAS 2015 ; Conference date: 13-10-2015 Through 16-10-2015",
year = "2015",
month = dec,
day = "23",
doi = "10.1109/ICCAS.2015.7364836",
language = "English",
series = "ICCAS 2015 - 2015 15th International Conference on Control, Automation and Systems, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1294--1296",
booktitle = "ICCAS 2015 - 2015 15th International Conference on Control, Automation and Systems, Proceedings",
}