article · Journal of Electrical Systems and Information Technology
Smart meters collect electricity consumption readings from users across modern electrical grids. Analysing these consumption patterns allows for effective consumer classification, supporting flexible demand management and improved energy control. An unsupervised classification approach using the K-means clustering algorithm groups households according to similarities in their typical electricity use. This method identifies consumption patterns by categorising temporal features extracted from smart meter readings into distinct groups. By organising similar consumption profiles together, the technique enables power suppliers to gain a clearer understanding of consumer habits, providing actionable information to support better operational decisions. The implementation was tested using real energy consumption data from 5567 London households collected during the UK Power Networks Low Carbon London project between November 2011 and February 2014.
Effective management of electricity networks relies on understanding when and how different households use energy. Grouping consumers by shared usage patterns helps electricity providers balance supply and demand more effectively. This provides practical insights that can support stable energy delivery and assist utility providers in planning demand management strategies based on actual household behaviours.
This method is aimed at power suppliers and grid operators seeking to implement flexible demand management and improve energy control strategies. By categorising smart meter readings based on temporal habits, it assists utility decision-makers in evaluating consumer energy profiles. Tested retrospectively on an open dataset of several thousand London households, the approach represents applied research evaluated on historical smart meter data, which requires real-time grid integration before deployment in live utility operations.
AI-generated from the published abstract. Always read the original work before citing.
Abstract Smart meter stores electricity consumption data of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer classification based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose an implementation of unsupervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. The main goal is to group similar observations together in order to easily look at the dataset. Hence, we go through pattern identification in households’ consumption with the K -means clustering algorithm. K -means clusters consumption behaviors based on extracted temporal features into k groups. The result from the algorithm helps power suppliers to understand power consumers’ better and helps them make better informed decision based on the information available to them. The dataset used in this paper is a real data from the London Data Store energy consumption readings for a sample of 5567 London Households that took part in the UK Power Networks Led Low Carbon London project between November 2011 and February 2014 available at: https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households .
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1186/s43067-023-00068-3
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
