Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation

With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods to segment...

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Autores principales:	Milad Afzalan, Farrokh Jazizadeh, Hoda Eldardiry
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Clustering methods energy segmentation demand response smart meters energy management two-stage clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a4ce48c09e1f4636b03523ac5ff3a1ec
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spelling	oai:doaj.org-article:a4ce48c09e1f4636b03523ac5ff3a1ec2021-11-17T00:00:28ZTwo-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation2169-353610.1109/ACCESS.2021.3122082https://doaj.org/article/a4ce48c09e1f4636b03523ac5ff3a1ec2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9584862/https://doaj.org/toc/2169-3536With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods to segment customers based on their load profiles, which help utilities and energy providers promote customized/personalized targeting for energy programs. Existing energy consumption segmentation techniques use assumptions that could reduce clusters’ quality in representing their members. Therefore, in this paper, we investigated a two-stage clustering method for capturing more representative load shape temporal patterns and peak demands through a cluster merging approach. In the first stage, load shapes are clustered (using classical clustering algorithms) by allowing a large number of clusters to accurately capture variations in energy use patterns, and cluster centroids are extracted by accounting for limited shape misalignment within the range of Demand Response (DR) timeframes. In the second stage, clusters with similar centroids and power magnitude ranges are merged using Complexity-Invariant Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the efficacy of the framework, compared to baseline methods, and discuss the impact on energy management. The proposed investigated merging-based clustering also increased correlation between cluster centroids and the corresponding members by 3–9% for different datasets.Milad AfzalanFarrokh JazizadehHoda EldardiryIEEEarticleClustering methodsenergy segmentationdemand responsesmart metersenergy managementtwo-stage clusteringElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151667-151680 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Clustering methods energy segmentation demand response smart meters energy management two-stage clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Clustering methods energy segmentation demand response smart meters energy management two-stage clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971 Milad Afzalan Farrokh Jazizadeh Hoda Eldardiry Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
description	With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods to segment customers based on their load profiles, which help utilities and energy providers promote customized/personalized targeting for energy programs. Existing energy consumption segmentation techniques use assumptions that could reduce clusters’ quality in representing their members. Therefore, in this paper, we investigated a two-stage clustering method for capturing more representative load shape temporal patterns and peak demands through a cluster merging approach. In the first stage, load shapes are clustered (using classical clustering algorithms) by allowing a large number of clusters to accurately capture variations in energy use patterns, and cluster centroids are extracted by accounting for limited shape misalignment within the range of Demand Response (DR) timeframes. In the second stage, clusters with similar centroids and power magnitude ranges are merged using Complexity-Invariant Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the efficacy of the framework, compared to baseline methods, and discuss the impact on energy management. The proposed investigated merging-based clustering also increased correlation between cluster centroids and the corresponding members by 3–9% for different datasets.
format	article
author	Milad Afzalan Farrokh Jazizadeh Hoda Eldardiry
author_facet	Milad Afzalan Farrokh Jazizadeh Hoda Eldardiry
author_sort	Milad Afzalan
title	Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
title_short	Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
title_full	Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
title_fullStr	Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
title_full_unstemmed	Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
title_sort	two-stage clustering of household electricity load shapes for improved temporal pattern representation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a4ce48c09e1f4636b03523ac5ff3a1ec
work_keys_str_mv	AT miladafzalan twostageclusteringofhouseholdelectricityloadshapesforimprovedtemporalpatternrepresentation AT farrokhjazizadeh twostageclusteringofhouseholdelectricityloadshapesforimprovedtemporalpatternrepresentation AT hodaeldardiry twostageclusteringofhouseholdelectricityloadshapesforimprovedtemporalpatternrepresentation
_version_	1718426066520899584

Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation

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