PUMPNET: a deep learning approach to pump operation detection

Ma, L; Meng, Q; Pan, S; Liebman, A

PUMPNET: a deep learning approach to pump operation detection

Ma, L Meng, Q Pan, S

Liebman, A

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Energy Informatics, 2021, 4, (1), pp. 1
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Ma, L
dc.contributor.author	Meng, Q
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Liebman, A
dc.date.accessioned	2022-04-27T08:55:18Z
dc.date.available	2022-04-27T08:55:18Z
dc.date.issued	2021-12-01
dc.identifier.citation	Energy Informatics, 2021, 4, (1), pp. 1
dc.identifier.issn	2520-8942
dc.identifier.issn	2520-8942
dc.identifier.uri	http://hdl.handle.net/10453/156678
dc.description.abstract	Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Energy Informatics
dc.relation.isbasedon	10.1186/s42162-020-00135-3
dc.rights	info:eu-repo/semantics/openAccess
dc.title	PUMPNET: a deep learning approach to pump operation detection
dc.type	Journal Article
utslib.citation.volume	4
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	2022-04-27T08:55:17Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	1

Abstract:

Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.

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http://hdl.handle.net/10453/156678