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The importance of Non-Intrusive Load Monitoring (NILM) has been increasingly recognized, given that NILM can enhance energy awareness and provide valuable insights for energy program design. Many existing NILM methods often rely on…

信号处理 · 电气工程与系统科学 2024-09-04 Xiangrui Li

With the help of smart metering valuable information of the appliance usage can be retrieved. In detail, non-intrusive load monitoring (NILM), also called load disaggregation, tries to identify appliances in the power draw of an household.…

其他计算机科学 · 计算机科学 2018-07-03 Dominik Egarter , Wilfried Elmenreich

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors…

机器学习 · 计算机科学 2021-02-09 Veronica Piccialli , Antonio M. Sudoso

Non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient…

系统与控制 · 电气工程与系统科学 2020-09-08 Elnaz Azizi , Mohammad TH Beheshti , Sadegh Bolouki

The rapid urbanization of developing countries coupled with explosion in construction of high rising buildings and the high power usage in them calls for conservation and efficient energy program. Such a program require monitoring of…

其他计算机科学 · 计算机科学 2017-03-13 Anthony Faustine , Nerey Henry Mvungi , Shubi Kaijage , Kisangiri Michael

Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem, aims to interpret the mains user electricity consumption into appliance level measurement. This article presents a new…

机器学习 · 计算机科学 2021-04-19 Sobhan Naderian

Non-intrusive load monitoring (NILM) or energy disaggregation aims to extract the load profiles of individual consumer electronic appliances, given an aggregate load profile of the mains of a smart home. This work proposes a novel…

Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. A lot…

Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to track electricity consumption of an individual appliance in a household by one aggregated single, such as building level meter readings. The goal of NILM is to…

机器学习 · 计算机科学 2023-03-08 Jinsong Wang , Kenneth A. Loparo

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption.…

信号处理 · 电气工程与系统科学 2023-06-09 Mohammad Irani Azad , Roozbeh Rajabi , Abouzar Estebsari

Non-Intrusive Load Monitoring (NILM) is a practical method to provide appliance-level electricity consumption information. Event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the…

信号处理 · 电气工程与系统科学 2019-03-25 Mengqi Lu , Zuyi Li

Non-Intrusive Load Monitoring (NILM) is a technology offering methods to identify appliances in homes based on their consumption characteristics and the total household demand. Recently, many different novel NILM approaches were introduced,…

其他计算机科学 · 计算机科学 2015-01-14 Dominik Egarter , Manfred Pöchacker , Wilfried Elmenreich

Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consumption of individual appliances can be estimated from the…

机器学习 · 计算机科学 2021-07-21 Antoine Langevin , Marc-André Carbonneau , Mohamed Cheriet , Ghyslain Gagnon

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach…

机器学习 · 计算机科学 2022-08-24 Govind Saraswat , Blake Lundstrom , Murti V Salapaka

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data…

信号处理 · 电气工程与系统科学 2021-08-05 Zhekai Du , Jingjing Li , Lei Zhu , Ke Lu , Heng Tao Shen

Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand…

机器学习 · 计算机科学 2024-04-01 Anže Pirnat , Blaž Bertalanič , Gregor Cerar , Mihael Mohorčič , Carolina Fortuna

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances. In this…

机器学习 · 计算机科学 2018-11-19 Changho Shin , Sunghwan Joo , Jaeryun Yim , Hyoseop Lee , Taesup Moon , Wonjong Rhee

Non-intrusive load monitoring (NILM) or energy disaggregation, aims to disaggregate a household's electricity consumption into constituent appliances. More than three decades of work in NILM has resulted in the development of several novel…

其他计算机科学 · 计算机科学 2014-10-09 Nipun Batra , Manoj Gulati , Puneet Jain , Kamin Whitehouse , Amarjeet Singh

Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and…

信号处理 · 电气工程与系统科学 2020-01-23 Christoph Klemenjak , Stephen Makonin , Wilfried Elmenreich

Energy disaggregation, also known as non-intrusive load monitoring (NILM), is the task of separating aggregate energy data for a whole building into the energy data for individual appliances. Studies have shown that simply providing…

动力系统 · 数学 2013-04-04 Roy Dong , Lillian Ratliff , Henrik Ohlsson , S. Shankar Sastry
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