Live Smart Autonomous Distributed Nonintrusive Load Monitoring With Open-Set, Multiappliance Load Identification and Online Load Noise Suppression and Elimination

IF 1.9 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Yu-Hsiu Lin, Yung-Yao Chen, Shih-Hao Wei
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引用次数: 0

Abstract

Effective demand-side management (DSM) can alleviate ever-increasing electricity demand required by, for instance, residential customers in downstream sectors of a smart grid. Different from traditional intrusive load monitoring conducted and used in a home energy management system (HEMS), energy disaggregation (i.e., nonintrusive load monitoring) can monitor relevant major electrical appliances in a nonintrusive and cost-effective fashion so as to ultimately achieve effective residential DSM. In this research, a live smart autonomous distributed energy disaggregation approach that is implemented in active edge-cloud collaborative computing is developed for live smart residential mains energy disaggregation at the edge, and its practical evaluation is showcased. The proposed approach is an autonomous edge-cloud collaborative two-phase energy disaggregation scheme that can achieve complex open-set load identification of single or multiple targeted unknown/new latent electrical appliances, whose implementation is horizontal federated learning-accommodated. The autonomous edge-cloud collaborative scheme implemented for live smart distributed energy disaggregation at the edge is a promising achievement, instead of transmitting raw private real-time load data to a central cloud server for traditional AI model training. In the two-phase energy disaggregation scheme, nonstationary/time-varying/unexpected nontargeted unknown loads as noisy loads including small loads can be combated during the offline load modeling and online live load monitoring phases for an enhanced achievement of smart distributed energy disaggregation at the edge. Practical evaluations are conducted and used to demonstrate the feasibility and effectiveness of the developed approach applied to a public electrical-end-use data (EEUD) dataset for live smart distributed residential mains energy disaggregation at the edge.

Abstract Image

具有开集、多设备负载识别和在线负载噪声抑制和消除的实时智能自主分布式非侵入式负载监测
有效的需求侧管理(DSM)可以缓解不断增长的电力需求,例如,智能电网下游部门的住宅客户。与传统的侵入式负荷监测在家庭能源管理系统(HEMS)中进行和使用不同,能量分解(即非侵入式负荷监测)可以以一种非侵入式和经济有效的方式监测相关主要电器,从而最终实现有效的住宅用电需求管理。本研究提出了一种基于主动边缘云协同计算的生活智能自主分布式能源分解方法,并对该方法进行了实用评估。该方法是一种自主的边缘云协同两相能量分解方案,可以实现单个或多个目标未知/新潜在电器的复杂开集负荷识别,其实现是水平联邦学习。在边缘实现实时智能分布式能源分解的自主边缘云协作方案是一项有前途的成果,而不是将原始的私有实时负载数据传输到中央云服务器以进行传统的人工智能模型训练。在两相能量分解方案中,非平稳/时变/意外的非目标未知负荷作为噪声负荷(包括小负荷)在离线负荷建模和在线实时负荷监测阶段进行对抗,增强了边缘智能分布式能量分解的实现。进行了实际评估,并用于证明将开发的方法应用于公共电力终端使用数据(EEUD)数据集的可行性和有效性,用于在边缘进行实时智能分布式住宅主电源能源分解。
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来源期刊
International Transactions on Electrical Energy Systems
International Transactions on Electrical Energy Systems ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
6.70
自引率
8.70%
发文量
342
期刊介绍: International Transactions on Electrical Energy Systems publishes original research results on key advances in the generation, transmission, and distribution of electrical energy systems. Of particular interest are submissions concerning the modeling, analysis, optimization and control of advanced electric power systems. Manuscripts on topics of economics, finance, policies, insulation materials, low-voltage power electronics, plasmas, and magnetics will generally not be considered for review.
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