期刊
2022 6TH INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2022)
卷 -, 期 -, 页码 221-225出版社
IEEE
DOI: 10.1109/ICGEA54406.2022.9791982
关键词
Socio-demographic information; residential electricity consumption; smart meter data; transfer learning
资金
- Electronics and Telecommunications Research Institute(ETRI) - Korean government [21ZK1100]
This paper proposes a framework for inferring socio-demographic information using smart meter data. It uses transfer learning methodology with datasets from different countries to train a deep learning model, and improves the model performance by instance selection and feature removal. The proposed method enhances the accuracy and performance of information inference.
This paper proposes a framework for inferring socio-demographic information using smart meter data. Sociodemographic information can be used to provide effective demand response programs and personalized services. Accordingly, research has been conducted to infer such information using electricity usage patterns which are collected by smart meters. However, collecting household characteristics information and corresponding smart meter data requires considerable effort and cost, making it difficult to obtain sufficient training data. Therefore, in this paper, we present a transfer learning methodology using datasets collected from different countries or regions. In the proposed framework, both the source dataset and target dataset are used to generate a typical daily load profile. The extracted daily load profiles are then used for instance selection step to prevent negative transfer. Also, to improve the performance of the transfer learning model, potentially noisy features are removed. The pre-trained deep learning model is then fine-tuned by the target dataset. Using the proposed method, the informationinferring performance is improved in classification accuracy, F1 score and area under the curve (AUC) metrics.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据