Joint Household Characteristic Prediction via Smart Meter Data
文献类型：期刊论文
| 作者 | Cong Y(丛杨) ; Fan HJ(范慧杰); Xu XW(徐晓伟); Yu HB(于海斌); Sun G(孙干); Hou DD(侯冬冬)
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| 刊名 | IEEE Transactions on Smart Grid
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| 出版日期 | 2018 |
| 页码 | 1-11 |
| ISSN号 | 1949-3053 |
| 关键词 | Household Characteristics Multi-task Learning Classification Problem Gaussian Process Smart Meter Data |
| 通讯作者 | Cong Y(丛杨) |
| 产权排序 | 1 |
| 中文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/21395]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 2.Department of Information Science, University of Arkansas at Little Rock, USA 3.University of Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | Cong Y,Fan HJ,Xu XW,et al. Joint Household Characteristic Prediction via Smart Meter Data[J]. IEEE Transactions on Smart Grid,2018:1-11. |
| APA | Cong Y,Fan HJ,Xu XW,Yu HB,Sun G,&Hou DD.(2018).Joint Household Characteristic Prediction via Smart Meter Data.IEEE Transactions on Smart Grid,1-11. |
| MLA | Cong Y,et al."Joint Household Characteristic Prediction via Smart Meter Data".IEEE Transactions on Smart Grid (2018):1-11. |
入库方式： OAI收割
来源：沈阳自动化研究所
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