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

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 smalle...

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Main Authors: A. Cano-Ortega, F. Sánchez-Sutil
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/3/517
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spelling doaj-8030238d32624991a42082537b9630dd2020-11-25T01:30:14ZengMDPI AGEnergies1996-10732020-01-0113351710.3390/en13030517en13030517Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in DwellingsA. Cano-Ortega0F. Sánchez-Sutil1Department of Electrical Engineering, University of Jaen, 23071 EPS Jaen, SpainDepartment of Electrical Engineering, University of Jaen, 23071 EPS Jaen, SpainThis 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.https://www.mdpi.com/1996-1073/13/3/517energy measurement device for dwellings (emdd)gateway lora network monitor (glnm)artificial bee colony (abc)load profileslora networkcloud computing
collection DOAJ
language English
format Article
sources DOAJ
author A. Cano-Ortega
F. Sánchez-Sutil
spellingShingle A. Cano-Ortega
F. Sánchez-Sutil
Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
Energies
energy measurement device for dwellings (emdd)
gateway lora network monitor (glnm)
artificial bee colony (abc)
load profiles
lora network
cloud computing
author_facet A. Cano-Ortega
F. Sánchez-Sutil
author_sort A. Cano-Ortega
title Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_short Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_full Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_fullStr Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_full_unstemmed Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_sort performance optimization lora network by artificial bee colony algorithm to determination of the load profiles in dwellings
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2020-01-01
description 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.
topic energy measurement device for dwellings (emdd)
gateway lora network monitor (glnm)
artificial bee colony (abc)
load profiles
lora network
cloud computing
url https://www.mdpi.com/1996-1073/13/3/517
work_keys_str_mv AT acanoortega performanceoptimizationloranetworkbyartificialbeecolonyalgorithmtodeterminationoftheloadprofilesindwellings
AT fsanchezsutil performanceoptimizationloranetworkbyartificialbeecolonyalgorithmtodeterminationoftheloadprofilesindwellings
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