{"title":"Implementation and Evaluation for Monitoring System in Electrical Meter based on LoRaWAN Network","authors":"A. F. Fauzi, I. G. D. Nugraha","doi":"10.1109/QIR54354.2021.9716177","DOIUrl":null,"url":null,"abstract":"AMI has been one of the leading technologies for Smart Grid. AMI utilized various network technologies to enable two-way communication for Smart Grid. AMI also enables real-time measurement that collects the electricity data from the user. This study utilizes LoRaWAN for the AMI and collects the electricity data. We focus on developing the web dashboard that visualizes the data from the LoRaWAN based AMI network. In addition, we utilize the anomaly detection module to analyze the collected data. From our experiment, we conduct pre-process for converting the LoRaWAN data. The result shows that the converted data is similar to actual data, and the accuracy of anomaly detection is 89%","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QIR54354.2021.9716177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
AMI has been one of the leading technologies for Smart Grid. AMI utilized various network technologies to enable two-way communication for Smart Grid. AMI also enables real-time measurement that collects the electricity data from the user. This study utilizes LoRaWAN for the AMI and collects the electricity data. We focus on developing the web dashboard that visualizes the data from the LoRaWAN based AMI network. In addition, we utilize the anomaly detection module to analyze the collected data. From our experiment, we conduct pre-process for converting the LoRaWAN data. The result shows that the converted data is similar to actual data, and the accuracy of anomaly detection is 89%
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