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Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

机译:人工蜂菌落算法的性能优化Lora网络确定住宅中负载型材的测定

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摘要

This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffic, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.
机译:本文提出了一种系统,用于使用从人造蜂菌落(ABC)的算法来提高长距离(LORA)网络的性能,该算法在LORA网络中获得最小分组丢失率(PLR),并允许更准确地确定居住的负载概况,具有较少的时间测量和更少的数据传输。开发算法计算Lora网络的配置参数,实时监视数据流量,并在网关Lora网络监视器(GLNM)中实现。已经开发了智能测量设备来确定住宅负载型材。用于住宅的这种能量测量装置(EMDD)测量每个家庭中电力的变量和消耗,可以配置测量时间。该研究还开发了GLNM网关,其监视并从安装的EMDDS上接收数据并使用Firebase将其上传到云。该开发系统允许对智能电网进行需求预测研究,对家庭消费分析,电费优化等。

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