Motif-Based Pattern Detection Method for Smart Energy Meter Data

Funde, Nitesh; Dhabu, Meera; Balande, Umesh

doi:10.1109/i2ct.2018.8529767

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Compared to the research fields above, only a few research studies have investigated the application of motif-based classification methods in power system related problems [10]- [12]. In these studies, motif-based discovery methods were applied to smart meter energy consumption data to identify regular behaviours, rules extraction and solar PV panel installations identification, respectively.…”

Section: Introductionmentioning

confidence: 99%

IRMAC: Interpretable Refined Motifs and Binary Classification for Rooftops PV Owners

Rui¹,

Pourmousavi²,

Soong³

et al. 2021

Preprint

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In this paper, we seek to identify residential rooftop solar PV owners using imported energy data. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose Interpretable Refined Motifs And binary Classification (IRMAC) method, which includes a shape-based dimensionality reduction technique we call Refined Motif (RM), and a classification technique with linear complexity to identify solar owners. Furthermore, with the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners as well as identifying electrical heating system users. The performances of the proposed method is studied and compared with various of state-of-the-art methods, where the proposed method outperformed the alternatives.

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Section: Introductionmentioning

confidence: 99%

IRMAC: Interpretable Refined Motifs and Binary Classification for Rooftops PV Owners

Rui¹,

Pourmousavi²,

Soong³

et al. 2021

Preprint

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show abstract

“…In this research, the LoRa Low Power Wide Area Network (LPWAN) network has been chosen, with transmission distances of up to 10 km. Comparison of standard load profiles with those obtained with SMs [27] Power consumption Optical sensor to SMs Analysis of measured data to improve electrical energy consumption [28] Energy consumption Commercial SMs Data analysis and clustering [29] Energy consumption ZigBee wireless Data analysis in different scenarios [30] Energy consumption Commercial SMs study and estimation of indicators of load profiles in dwellings [31] Energy consumption Commercial SMs Measure time 15 min Obtaining and analysis of load profiles in houses in Evora (Portugal) [32] Energy consumption Commercial SMs Measure time 15 min Obtaining and analysis of load profiles in 1000 houses in Poland [33] Energy consumption Commercial SMs Measure time 30 min Smart meter dataset Data Analysis and Clustering [34] Energy consumption Commercial SMs…”

Section: Introductionmentioning

confidence: 99%

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

Caño

Sánchez-Sutil

2020

Energies

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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.

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Smart meter for residential electricity consumption with TLBO algorithm for LoRaWAN

2023

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Motif-Based Pattern Detection Method for Smart Energy Meter Data

Cited by 4 publications

References 12 publications

IRMAC: Interpretable Refined Motifs and Binary Classification for Rooftops PV Owners

IRMAC: Interpretable Refined Motifs and Binary Classification for Rooftops PV Owners

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

Smart meter for residential electricity consumption with TLBO algorithm for LoRaWAN

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