基于深度学习的非侵入式负荷监测与来自智能电表的低分辨率数据

IF 0.3 Q4 MATHEMATICS
Marco Manca, L. Massidda
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引用次数: 1

摘要

详细了解家庭中电器的能耗和激活状态对用户和能源供应商都是有益的,可以提高能源意识,并通过需求响应技术实施消费管理政策。监测单个电器的消费当然是昂贵的,而且很难在技术上大规模地实施,因此已经开发了非侵入式监测技术,使电器的消费能够从对房屋总消费的唯一测量中得出。然而,这些方法通常需要在家庭系统中安装额外的硬件,以高时间分辨率测量总能耗。在这项工作中,我们使用深度学习方法来分解在意大利部署的新一代智能电表直接产生的低频能量信号,而不需要额外的特定硬件。在两个参考数据集上获得了良好的性能,证明了该方法的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning based non-intrusive load monitoring with low resolution data from smart meters
Abstract A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficial for both the user and the energy supplier, improving energy awareness and allowing the implementation of consumption management policies through demand response techniques. Monitoring the consumption of individual appliances is certainly expensive and difficult to implement technically on a large scale, so non-intrusive monitoring techniques have been developed that allow the consumption of appliances to be derived from the sole measurement of the aggregate consumption of a house. However, these methodologies often require additional hardware to be installed in the domestic system to measure total energy consumption with high temporal resolution. In this work we use a deep learning method to disaggregate the low frequency energy signal generated directly by the new generation smart meters deployed in Italy, without the need of additional specific hardware. The performances obtained on two reference datasets are promising and demonstrate the applicability of the proposed approach.
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来源期刊
CiteScore
1.30
自引率
0.00%
发文量
3
审稿时长
16 weeks
期刊介绍: Communications in Applied and Industrial Mathematics (CAIM) is one of the official journals of the Italian Society for Applied and Industrial Mathematics (SIMAI). Providing immediate open access to original, unpublished high quality contributions, CAIM is devoted to timely report on ongoing original research work, new interdisciplinary subjects, and new developments. The journal focuses on the applications of mathematics to the solution of problems in industry, technology, environment, cultural heritage, and natural sciences, with a special emphasis on new and interesting mathematical ideas relevant to these fields of application . Encouraging novel cross-disciplinary approaches to mathematical research, CAIM aims to provide an ideal platform for scientists who cooperate in different fields including pure and applied mathematics, computer science, engineering, physics, chemistry, biology, medicine and to link scientist with professionals active in industry, research centres, academia or in the public sector. Coverage includes research articles describing new analytical or numerical methods, descriptions of modelling approaches, simulations for more accurate predictions or experimental observations of complex phenomena, verification/validation of numerical and experimental methods; invited or submitted reviews and perspectives concerning mathematical techniques in relation to applications, and and fields in which new problems have arisen for which mathematical models and techniques are not yet available.
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