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Joint Household Characteristic Prediction via Smart Meter Data
Sun G(孙干); Cong Y(丛杨); Hou DD(侯冬冬); Fan HJ(范慧杰); Xu XW(徐晓伟); Yu HB(于海斌)
Department机器人学研究室
Source PublicationIEEE Transactions on Smart Grid
ISSN1949-3053
2019
Volume10Issue:2Pages:1834-1844
Indexed BySCI ; EI
EI Accession number20175004531749
WOS IDWOS:000459504600061
Contribution Rank1
KeywordHousehold Characteristics Multi-task Learning Classification Problem Gaussian Process Smart Meter Data
Abstract

Predicting specific household characteristics (e.g., age of person, household income, cooking style, etc) from their everyday electricity consumption (i.e., smart meter data) enables energy provider to develop many intelligent business applications or help consumers to reduce their energy consumption. However, most existing works intend to predict single household characteristic via smart meter data independently, and ignore the joint analysis of different characteristics. In this paper, we consider each characteristic as an independent task and intend to predict multiple household characteristics simultaneously by designing a new multi-task learning formulation: Discriminative Multi- Task Relationship Learning (DisMTRL). Specifically, two main challenges need to be handled: 1) task relationship, that is the embedded structure of relationships among different characteristics; 2) feature learning, there exist redundant features in original training data. To achieve these, our DisMTRL model aims to obtain a simple but robust weight matrix through capturing the intrinsic relatedness among different characteristics by task covariance matrix (MTRL) and incorporating the discriminative features via feature covariance matrix (Dis). For model optimization, we employ an alternating minimization strategy to learn the optimal weight matrix as well as the relationship between tasks by converting feature learning regularization as trace minimization problem. For evaluation, we adopt a smart meter dataset collected from 4232 households in Ireland at a 30min granularity over an interval of 1.5 years. The experimental results justify the effectiveness of our proposed model.

Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21395
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences, China
3.Department of Information Science, University of Arkansas at Little Rock, USA
Recommended Citation
GB/T 7714
Sun G,Cong Y,Hou DD,et al. Joint Household Characteristic Prediction via Smart Meter Data[J]. IEEE Transactions on Smart Grid,2019,10(2):1834-1844.
APA Sun G,Cong Y,Hou DD,Fan HJ,Xu XW,&Yu HB.(2019).Joint Household Characteristic Prediction via Smart Meter Data.IEEE Transactions on Smart Grid,10(2),1834-1844.
MLA Sun G,et al."Joint Household Characteristic Prediction via Smart Meter Data".IEEE Transactions on Smart Grid 10.2(2019):1834-1844.
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