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SMART METER DESIGN AND IMPLEMENTATION USING LOAD FORECASTING TECHNIQUE

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dc.contributor.author Amankhan, Assilkhan
dc.contributor.author Kural, Askat
dc.date.accessioned 2021-01-25T09:24:02Z
dc.date.available 2021-01-25T09:24:02Z
dc.date.issued 2020-04-27
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/5236
dc.description.abstract This study has specifically focused to develop a multi-functional Smart Meter (SM) which would be able to address some of the challenges currently available in the conventional digital automated metering system in Eurasia. SMs with its unique performance with the Internet of Things (IoT) tend to be an efficient system for electricity management, secure against the intervention by third parties, and reliable for real-time remote monitoring and tracking via mobile application as well as web application. IoT-based architecture of SM is proposed along with employing anti-theft algorithms and Long Short-Term Memory (LSTM) load forecasting that can assist in maintaining energy management systems [1]. For industrial purpose, SM enables the customers to monitor the ageing and vibration of the transformer in order to adjust loading for the life extension of the transformer. The consumption patterns obtained by SM are studied and discussed. en_US
dc.language.iso en en_US
dc.publisher Nazarbayev University School of Engineering and Digital Sciences en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Long Short-Term Memory en_US
dc.subject LSTM en_US
dc.subject Smart Meter en_US
dc.subject Load Forecasting technique en_US
dc.subject Internet of Things en_US
dc.subject IoT en_US
dc.subject SM en_US
dc.subject Research Subject Categories::TECHNOLOGY en_US
dc.title SMART METER DESIGN AND IMPLEMENTATION USING LOAD FORECASTING TECHNIQUE en_US
dc.type Capstone Project en_US
workflow.import.source science


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Attribution-NonCommercial-ShareAlike 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States