An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load patt...

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Published in:	Sensors (Basel, Switzerland) Vol. 21; no. 19; p. 6466
Main Authors:	Jiang, Zigui, Lin, Rongheng, Yang, Fangchun
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 28.09.2021 MDPI
Subjects:	Algorithms Artificial intelligence Behavior Blockchain Cluster Analysis Clustering Computer Systems Consumers Consumption data stream clustering Distance learning Electricity incremental learning Internet of Things load pattern Machine learning Probability Smart grid technology smart meter data Wavelet transforms data stream clustering incremental learning load pattern smart meter data
ISSN:	1424-8220, 1424-8220
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Abstract	The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.
AbstractList	The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results. The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.
Author	Jiang, Zigui Yang, Fangchun Lin, Rongheng
AuthorAffiliation	2 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; fcyang@bupt.edu.cn 1 School of Software Engineering, Sun Yat-Sen University, Zhuhai 519082, China; jiangzg3@mail.sysu.edu.cn
AuthorAffiliation_xml	– name: 2 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; fcyang@bupt.edu.cn – name: 1 School of Software Engineering, Sun Yat-Sen University, Zhuhai 519082, China; jiangzg3@mail.sysu.edu.cn
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Cites_doi	10.1109/TII.2017.2769450 10.31209/2019.100000113 10.1016/j.sigpro.2015.10.014 10.1007/s10115-014-0808-1 10.1016/j.energy.2011.12.031 10.1109/TST.2015.7085625 10.1016/j.knosys.2016.02.001 10.1137/1.9781611972795.63 10.1016/j.eswa.2019.113069 10.1145/2806416.2806464 10.1186/s41044-016-0011-3 10.1109/ACCESS.2020.2977149 10.1109/TII.2016.2528819 10.1007/978-3-642-22351-8_24 10.3390/en13112762 10.1016/j.enbuild.2019.04.034 10.1145/3335484.3335517 10.3390/s21093293 10.2298/CSIS200330012S 10.1016/j.apenergy.2020.114915 10.3390/en11092235 10.1109/JIOT.2020.3030308 10.1007/s10462-020-09874-x 10.3390/s21093157 10.1016/j.neucom.2017.06.084 10.1109/TASLP.2016.2530409 10.1109/TSG.2015.2446935 10.1016/j.is.2016.06.007 10.1109/TII.2017.2785963 10.1109/TNNLS.2017.2716952 10.1109/TSMC.2018.2791511 10.3390/s21051564 10.1109/TSG.2016.2548565 10.1049/iet-gtd.2014.0658 10.1109/TII.2020.2998479 10.1016/j.apenergy.2017.07.114 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00187 10.1007/s42044-019-00043-0 10.1109/IoT52625.2021.9469717 10.3390/en13215739 10.1109/TII.2017.2684807 10.1016/j.ins.2016.12.004 10.1007/s40745-015-0040-1
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Copyright	2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 by the authors. 2021
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StartPage	6466
SubjectTerms	Algorithms Artificial intelligence Behavior Blockchain Cluster Analysis Clustering Computer Systems Consumers Consumption data stream clustering Distance learning Electricity incremental learning Internet of Things load pattern Machine learning Probability Smart grid technology smart meter data Wavelet transforms
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Title	An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System
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