An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset

Mingchao Li; Shuai Han; Jonathan Shi

doi:10.1016/j.enbuild.2017.02.006

An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset

Mingchao Li, Shuai Han, Jonathan Shi

Research output: Journal article publication › Journal article › Academic research › peer-review

28 Citations (Scopus)

Abstract

An electric meter is used for measuring the electricity consumption in a household. The obtained total data doesn't separate the consumptions of individual appliances and provides little actionable information for energy efficiency improvement. This paper presents an enhanced Iterative Self-Organizing Data Analysis (ISODATA) algorithm which can identify the operating modes and electricity consumptions of major appliances based on the metered total electricity consumption. The enhanced ISODATA algorithm is improved with k-medoids and parameter selections. It can overcome various drawbacks of the original k-means and the original ISODATA algorithm. Validated with actual data, the presented method is able to disaggregate the total electricity consumption efficiently and accurately into categories corresponding to major end uses.

Original language	English
Pages (from-to)	305-316
Number of pages	12
Journal	Energy and Buildings
Volume	140
DOIs	https://doi.org/10.1016/j.enbuild.2017.02.006
Publication status	Published - 1 Apr 2017

Keywords

Clustering
Electrical appliance
ISODATA
Pattern recognition
Unsupervised learning

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction
Mechanical Engineering
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.enbuild.2017.02.006

Cite this

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abstract = "An electric meter is used for measuring the electricity consumption in a household. The obtained total data doesn't separate the consumptions of individual appliances and provides little actionable information for energy efficiency improvement. This paper presents an enhanced Iterative Self-Organizing Data Analysis (ISODATA) algorithm which can identify the operating modes and electricity consumptions of major appliances based on the metered total electricity consumption. The enhanced ISODATA algorithm is improved with k-medoids and parameter selections. It can overcome various drawbacks of the original k-means and the original ISODATA algorithm. Validated with actual data, the presented method is able to disaggregate the total electricity consumption efficiently and accurately into categories corresponding to major end uses.",

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AB - An electric meter is used for measuring the electricity consumption in a household. The obtained total data doesn't separate the consumptions of individual appliances and provides little actionable information for energy efficiency improvement. This paper presents an enhanced Iterative Self-Organizing Data Analysis (ISODATA) algorithm which can identify the operating modes and electricity consumptions of major appliances based on the metered total electricity consumption. The enhanced ISODATA algorithm is improved with k-medoids and parameter selections. It can overcome various drawbacks of the original k-means and the original ISODATA algorithm. Validated with actual data, the presented method is able to disaggregate the total electricity consumption efficiently and accurately into categories corresponding to major end uses.

KW - Clustering

KW - Electrical appliance

KW - ISODATA

KW - Pattern recognition

KW - Unsupervised learning

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JO - Energy and Buildings

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ER -

An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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