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

Abstract

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.

Design and Testing of a Power Analyzer Monitor and Programming Device in Industries with a LoRA LPWAN Network

“…Raza et al [29] studied the operating bases of the components integrated in a LoRaWAN for applications in industry 4.0. Cano-Ortega et al [30] developed an ABC-based algorithm for the optimization of the configuration parameters of the LoRa LPWAN network in real time to achieve the best performance of the data traffic in real time with a minimum loss of information packets. Dawaliby et al [31] studied the optimization of the spreading factor configuration parameter of the LoRa LPWAN network in smart cities.…”

“…Another added feature is that it runs on Raspberry Pi (Raspberry Pi, Cambridge, UK) from model 3 onwards, meaning that additional software can be installed. The network optimization algorithm proposed by Cano et al [30] is implemented on Raspberry.…”

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

“…More specifically, the choice of a temporal data granularity (data sampling frequency) for specifying consumption load profile features has a crucial impact on the results of any action or assessment, as discussed in the literature [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], see Table 1 . This table summarizes for each potential action or assessment the time resolution (data granularity) and time horizon (time slice) envisaged for the works related to load profiles in households.…”

“…Few references focused on granularities lower than 15 s. High data granularity naturally implies larger amounts of data to be locally stored (either hard disk or memory card) or uploaded to the cloud. This has led many researchers and industry practitioners to develop and survey a vast number of analytical tools that could help to segment and cluster SM big data so that they can be analyzed in real time [ 11 , 32 ]. On the other hand, uploading these data to the cloud (data traffic with the cloud) is another important limitation [ 11 , 44 , 53 ].…”

“…This has led many researchers and industry practitioners to develop and survey a vast number of analytical tools that could help to segment and cluster SM big data so that they can be analyzed in real time [ 11 , 32 ]. On the other hand, uploading these data to the cloud (data traffic with the cloud) is another important limitation [ 11 , 44 , 53 ]. High granularity requires a large bandwidth, which is not always available in individual households.…”