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Article: Deep learning-based socio-demographic information identification from smart meter data

TitleDeep learning-based socio-demographic information identification from smart meter data
Authors
Keywordsbig data
classification
Convolutional neural network (CNN)
deep learning
smart meter
socio-demographic information
support vector machine (SVM)
Issue Date2019
Citation
IEEE Transactions on Smart Grid, 2019, v. 10, n. 3, p. 2593-2602 How to Cite?
AbstractSmart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.
Persistent Identifierhttp://hdl.handle.net/10722/308744
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.863
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Yi-
dc.contributor.authorChen, Qixin-
dc.contributor.authorGan, Dahua-
dc.contributor.authorYang, Jingwei-
dc.contributor.authorKirschen, Daniel S.-
dc.contributor.authorKang, Chongqing-
dc.date.accessioned2021-12-08T07:50:02Z-
dc.date.available2021-12-08T07:50:02Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Smart Grid, 2019, v. 10, n. 3, p. 2593-2602-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/308744-
dc.description.abstractSmart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.subjectbig data-
dc.subjectclassification-
dc.subjectConvolutional neural network (CNN)-
dc.subjectdeep learning-
dc.subjectsmart meter-
dc.subjectsocio-demographic information-
dc.subjectsupport vector machine (SVM)-
dc.titleDeep learning-based socio-demographic information identification from smart meter data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSG.2018.2805723-
dc.identifier.scopuseid_2-s2.0-85042102008-
dc.identifier.volume10-
dc.identifier.issue3-
dc.identifier.spage2593-
dc.identifier.epage2602-
dc.identifier.isiWOS:000466603800023-

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