国内智能计量基础设施和一种使用低速率功耗数据的家用电器识别方法

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ioannis Paraskevas, Maria Barbarosou, Richard Fitton, William Swan
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引用次数: 5

摘要

英国国内智能计量基础设施的部署为利用非侵入式负载监测方法识别家用电器提供了机会。确定某些家用电器的能源消耗,可为能源供应商及其他对能源消耗有兴趣的机构提供有用的见解。因此,作为智慧城市基础设施组成部分的国内智能计量系统,考虑到系统的局限性,也可以用于家电识别目的。在本文中,介绍了通过外部消费者访问设备直接从家用智能电表访问数据的逐步描述,以及一种易于实现的方法,用于通过以类似于家用智能计量系统可用的速率采样的功耗信号来识别常用家用电器。实验结果表明,从一维/二维离散傅立叶变换或离散余弦变换中提取的时域和频域特征相结合,比单独使用时域和频域特征具有更好的识别性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Domestic smart metering infrastructure and a method for home appliances identification using low-rate power consumption data

Domestic smart metering infrastructure and a method for home appliances identification using low-rate power consumption data

The deployment of domestic smart metering infrastructure in Great Britain provides the opportunity for identification of home appliances utilising non-intrusive load monitoring methods. Identifying the energy consumption of certain home appliances generates useful insights for the energy suppliers and for other bodies with a vested interest in energy consumption. Consequently, the domestic smart metering system, which is an integral part of the smart cities' infrastructure, can also be used for home appliance identification purposes taking into account the limitations of the system. In this article, a step-by-step description on accessing data directly from the domestic Smart Meter via an external Consumer Access Device is described, as well as an easy-to-implement method for identifying commonly used home appliances through their power consumption signals sampled at a rate similar to the rate available by the domestic smart metering system. The experimental results indicate that the combination of time domain with frequency domain features extracted either from the 1D/2D Discrete Fourier Transform or the Discrete Cosine Transform provides improved recognition performance compared to the case where the time domain or the frequency domain features are used separately.

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来源期刊
IET Smart Cities
IET Smart Cities Social Sciences-Urban Studies
CiteScore
7.70
自引率
3.20%
发文量
25
审稿时长
21 weeks
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