Estimation of household characteristics with uncertainties from smart meter data

Lin, J; Ma, J; Zhu, JG

Estimation of household characteristics with uncertainties from smart meter data

Lin, J Ma, J Zhu, JG

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: International Journal of Electrical Power and Energy Systems, 2022, 143
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Lin, J
dc.contributor.author	Ma, J
dc.contributor.author	Zhu, JG
dc.date.accessioned	2023-04-11T03:33:37Z
dc.date.available	2023-04-11T03:33:37Z
dc.date.issued	2022-12-01
dc.identifier.citation	International Journal of Electrical Power and Energy Systems, 2022, 143
dc.identifier.issn	0142-0615
dc.identifier.issn	1879-3517
dc.identifier.uri	http://hdl.handle.net/10453/169544
dc.description.abstract	The knowledge of household characteristics can help energy providers carry out more personalized demand-side management programs. Obtaining such information through surveys is costly and time-consuming in practice. This paper proposes a novel estimation method for household characteristics with uncertainties using the residential electricity consumption data. To alleviate the class imbalance problem in the dataset, a dynamic time warping sampling (DTWS) method is proposed to generate synthetic data for the minority class. To overcome the problem that the existing methods for identifying household characteristics cannot provide the confidence level of the results, a Bayesian convolutional neural network (BCNN) model is developed for feature extraction and characteristic identification with uncertainties. These quantified uncertainties can be regarded as a measure of confidence and can be used to target customers more effectively for energy efficiency and demand response programs. The effectiveness of the proposed model is validated by experiments on ground truth data.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	International Journal of Electrical Power and Energy Systems
dc.relation.isbasedon	10.1016/j.ijepes.2022.108440
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.title	Estimation of household characteristics with uncertainties from smart meter data
dc.type	Journal Article
utslib.citation.volume	143
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-11T03:33:36Z
pubs.publication-status	Published
pubs.volume	143

Abstract:

The knowledge of household characteristics can help energy providers carry out more personalized demand-side management programs. Obtaining such information through surveys is costly and time-consuming in practice. This paper proposes a novel estimation method for household characteristics with uncertainties using the residential electricity consumption data. To alleviate the class imbalance problem in the dataset, a dynamic time warping sampling (DTWS) method is proposed to generate synthetic data for the minority class. To overcome the problem that the existing methods for identifying household characteristics cannot provide the confidence level of the results, a Bayesian convolutional neural network (BCNN) model is developed for feature extraction and characteristic identification with uncertainties. These quantified uncertainties can be regarded as a measure of confidence and can be used to target customers more effectively for energy efficiency and demand response programs. The effectiveness of the proposed model is validated by experiments on ground truth data.

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http://hdl.handle.net/10453/169544