{"title":"使用AGILASx -菲律宾常用电器数据集的ILM电器识别系统","authors":"M. Villanueva, Samuel Matthew G. Dumlao, R. Reyes","doi":"10.1109/CIOT.2016.7872910","DOIUrl":null,"url":null,"abstract":"This study presents the development of a system which can automatically recognize home appliances based on a dataset of electric consumption profiles. The authors report the creation of AGILASx, a dataset of 50 common home appliances and devices in the Philippines. The dataset is populated with 100 appliance signatures in .XML format acquired using plug-based sensors. Each appliance signature consists of the following electric characteristics: real power (W), apparent power (VA), reactive power (var), RMS current (A), RMS voltage (V) and Power Factor (PF). A machine learning approach was utilized for the recognition experiment following a set of test protocols — intersession and unseen instances. The baseline recognition algorithm used was the k-Nearest Neighbor (k-NN) for both test protocols and accuracy levels were collected over three different acquisition frequencies. Using results of the confusion matrices, best results were observed at acquisition frequency of 10−1 Hz for intersession (99%) and unseen instance (99%) test protocols. Lastly, to integrate the dataset and the recognition algorithm, a web application was developed adapting a Web-of-Things architecture to present a smart of recognized appliances and their corresponding consumption.","PeriodicalId":222295,"journal":{"name":"2016 Cloudification of the Internet of Things (CIoT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Appliance recognition system for ILM using AGILASx — Dataset of common appliances in the Philippines\",\"authors\":\"M. Villanueva, Samuel Matthew G. Dumlao, R. Reyes\",\"doi\":\"10.1109/CIOT.2016.7872910\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents the development of a system which can automatically recognize home appliances based on a dataset of electric consumption profiles. The authors report the creation of AGILASx, a dataset of 50 common home appliances and devices in the Philippines. The dataset is populated with 100 appliance signatures in .XML format acquired using plug-based sensors. Each appliance signature consists of the following electric characteristics: real power (W), apparent power (VA), reactive power (var), RMS current (A), RMS voltage (V) and Power Factor (PF). A machine learning approach was utilized for the recognition experiment following a set of test protocols — intersession and unseen instances. The baseline recognition algorithm used was the k-Nearest Neighbor (k-NN) for both test protocols and accuracy levels were collected over three different acquisition frequencies. Using results of the confusion matrices, best results were observed at acquisition frequency of 10−1 Hz for intersession (99%) and unseen instance (99%) test protocols. Lastly, to integrate the dataset and the recognition algorithm, a web application was developed adapting a Web-of-Things architecture to present a smart of recognized appliances and their corresponding consumption.\",\"PeriodicalId\":222295,\"journal\":{\"name\":\"2016 Cloudification of the Internet of Things (CIoT)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Cloudification of the Internet of Things (CIoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIOT.2016.7872910\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Cloudification of the Internet of Things (CIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIOT.2016.7872910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Appliance recognition system for ILM using AGILASx — Dataset of common appliances in the Philippines
This study presents the development of a system which can automatically recognize home appliances based on a dataset of electric consumption profiles. The authors report the creation of AGILASx, a dataset of 50 common home appliances and devices in the Philippines. The dataset is populated with 100 appliance signatures in .XML format acquired using plug-based sensors. Each appliance signature consists of the following electric characteristics: real power (W), apparent power (VA), reactive power (var), RMS current (A), RMS voltage (V) and Power Factor (PF). A machine learning approach was utilized for the recognition experiment following a set of test protocols — intersession and unseen instances. The baseline recognition algorithm used was the k-Nearest Neighbor (k-NN) for both test protocols and accuracy levels were collected over three different acquisition frequencies. Using results of the confusion matrices, best results were observed at acquisition frequency of 10−1 Hz for intersession (99%) and unseen instance (99%) test protocols. Lastly, to integrate the dataset and the recognition algorithm, a web application was developed adapting a Web-of-Things architecture to present a smart of recognized appliances and their corresponding consumption.