Cluster Analysis and Model Comparison Using Smart Meter Data.

Shaukat, MA; Shaukat, HR; Qadir, Z; Munawar, HS; Kouzani, AZ; Mahmud, MAP

Cluster Analysis and Model Comparison Using Smart Meter Data.

Shaukat, MA Shaukat, HR Qadir, Z Munawar, HS Kouzani, AZ Mahmud, MAP

Permalink

Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2021, 21, (9), pp. 3157
Issue Date:: 2021-05-02

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (2.56 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Shaukat, MA
dc.contributor.author	Shaukat, HR
dc.contributor.author	Qadir, Z
dc.contributor.author	Munawar, HS
dc.contributor.author	Kouzani, AZ
dc.contributor.author	Mahmud, MAP
dc.date.accessioned	2022-03-21T23:55:14Z
dc.date.available	2021-04-27
dc.date.available	2022-03-21T23:55:14Z
dc.date.issued	2021-05-02
dc.identifier.citation	Sensors (Basel), 2021, 21, (9), pp. 3157
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/155442
dc.description.abstract	Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.
dc.language	eng
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s21093157
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Cluster Analysis and Model Comparison Using Smart Meter Data.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-21T23:55:13Z
pubs.issue	9
pubs.publication-status	Published online
pubs.volume	21
utslib.citation.issue	9

Abstract:

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155442