A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Tavakoli, S; Khalilpour, K

A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Tavakoli, S Khalilpour, K

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ENERGIES, 2021, 14, (16)
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Tavakoli, S
dc.contributor.author	Khalilpour, K https://orcid.org/0000-0003-4104-2488
dc.date.accessioned	2022-02-25T22:54:04Z
dc.date.available	2022-02-25T22:54:04Z
dc.date.issued	2021-08-01
dc.identifier.citation	ENERGIES, 2021, 14, (16)
dc.identifier.issn	1996-1073
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/10453/154863
dc.description.abstract	<jats:p>The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ENERGIES
dc.relation.isbasedon	10.3390/en14164880
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.title	A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2022-02-25T22:54:01Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	16

Abstract:

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.

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http://hdl.handle.net/10453/154863