Author:
Chaudhari Archana Yashodip,Mulay Preeti
Abstract
Purpose
To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.
Design/methodology/approach
This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.
Findings
Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.
Originality/value
ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.
Subject
Strategy and Management,General Energy
Reference59 articles.
1. Archive, I.S.S.D. (2012), “Data from the commission for energy regulation (CER) – smart metering project”, available: www.ucd.ie/issda/data/commissionforenergyregulationcer/ (April 20, 2018).
2. Clustering household energy-saving behaviours by behavioural attribute;Energy Policy,2016
3. RNN-DBSCAN: a density-based clustering algorithm using reverse nearest neighbor density estimates;IEEE Transactions on Knowledge and Data Engineering,2018
4. Analysis and study of incremental K-means clustering algorithm,2011
5. SCSI: Real-Time data analysis with Cassandra and Spark,2019
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献