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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Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 3; p. 517
Main Authors Cano-Ortega, A., Sánchez-Sutil, F.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 2020
Subjects
Access to information
Accuracy
Algorithms
artificial bee colony (abc)
cloud computing
Data transmission
Dwellings
Economic forecasting
Electric power rates
Electrical loads
Electricity
Electricity consumption
Electricity pricing
Energy consumption
Energy management
Energy measurement
energy measurement device for dwellings (emdd)
gateway lora network monitor (glnm)
Gateways
Generators
Genetic algorithms
load profiles
lora network
Optimization
Optimization algorithms
Real time
Search algorithms
Variables
Wide area networks
Wireless networks
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13030517