通过迁移学习使用智能电表数据推断社会人口信息

Myung-Gil Kim, Dongju Kim, E. Hwang, Eden Kim, Seok-Gap Ko, Byung-Tak Lee
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摘要

本文提出了一个使用智能电表数据推断社会人口信息的框架。社会人口统计信息可用于提供有效的需求响应方案和个性化服务。因此,已经进行了研究,利用智能电表收集的用电模式来推断这些信息。然而,收集家庭特征信息和相应的智能电表数据需要相当的努力和成本,难以获得足够的培训数据。因此,在本文中,我们提出了一种使用来自不同国家或地区的数据集的迁移学习方法。在提出的框架中,源数据集和目标数据集都用于生成典型的每日负载概况。然后将提取的日负荷概况用于实例选择步骤,以防止负转移。此外,为了提高迁移学习模型的性能,去除了潜在的噪声特征。然后通过目标数据集对预训练的深度学习模型进行微调。利用该方法,在分类精度、F1分数和曲线下面积(AUC)指标上提高了信息推断性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inferring Socio-Demographic Information Using Smart Meter Data by Transfer Learning
This paper proposes a framework for inferring socio-demographic information using smart meter data. Socio-demographic 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 information-inferring performance is improved in classification accuracy, F1 score and area under the curve (AUC) metrics.
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