scholarly journals A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1446 ◽  
Author(s):  
Fateh Melzi ◽  
Allou Same ◽  
Mohamed Zayani ◽  
Latifa Oukhellou
Keyword(s):  
Mixture Model ◽  
Electricity Consumption ◽  
Smart Meter

scholarly journals Electricity Consumption Clustering Using Smart Meter Data

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 859 ◽  
Author(s):  
Alexander Tureczek ◽  
Per Nielsen ◽  
Henrik Madsen
Keyword(s):  
Electricity Consumption ◽  
Smart Meter

scholarly journals Smart metering system data analytics platform using multicore edge computing

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Juan C. Olivares-Rojas ◽  
Enrique Reyes-Archundia ◽  
José A. Gutiérrez-Gnecchi ◽  
Ismael Molina-Moreno ◽  
Adriana C. Téllez-Anguiano ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Data Processing ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Edge Computing ◽  
Smart Meter ◽  
Smart Metering ◽  
Smart Meters ◽  
System Data

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

scholarly journals A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2235 ◽  
Author(s):  
Zigui Jiang ◽  
Rongheng Lin ◽  
Fangchun Yang
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Electricity Consumption ◽  
Learning Model ◽  
Unsupervised Clustering ◽  
Smart Meter ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Comparison Results ◽  
Hybrid Machine

Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy.

scholarly journals Development of electricity consumption profiles of residential buildings based on smart meter data clustering

2021 ◽  
pp. 111376
Author(s):  
László Czétány ◽  
Viktória Vámos ◽  
Miklós Horváth ◽  
Zsuzsa Szalay ◽  
Adrián Mota-Babiloni ◽  
...  
Keyword(s):  
Data Clustering ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Smart Meter

scholarly journals An IOT based smart metering development for energy management system

2019 ◽  
Vol 9 (4) ◽  
pp. 3041 ◽  
Author(s):  
S.G Priyadharshini ◽  
C. Subramani ◽  
J. Preetha Roselyn
Keyword(s):  
Remote Monitoring ◽  
Energy Demand ◽  
Demand Management ◽  
Electricity Consumption ◽  
Smart Meter ◽  
End User ◽  
Labview Software ◽  
The Past ◽  
To Come ◽  
Smart Grid Technology

<p>The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters.</p>

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