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Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances. In this way, considerable…

机器学习 · 计算机科学 2021-06-02 Yu Zhang , Guoming Tang , Qianyi Huang , Yi Wang , Hong Xu

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

The growing global energy demand and the urgent need for sustainability call for innovative ways to boost energy efficiency. While advanced energy-saving systems exist, they often fall short without user engagement. Providing feedback on…

机器学习 · 计算机科学 2025-05-13 Sotirios Athanasoulias

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, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the energy consumption of each appliance given the aggregate signal recorded by a single smart meter. In this paper, we propose…

最优化与控制 · 数学 2022-04-13 Marco Balletti , Veronica Piccialli , Antonio M. Sudoso

The housing structures have changed with urbanization and the growth due to the construction of high-rise buildings all around the world requires end-use appliance energy conservation and management in real-time. This shift also came along…

信号处理 · 电气工程与系统科学 2021-04-16 Akriti Verma , Adnan Anwar , M. A. Parvez Mahmud , Mohiuddin Ahmed , Abbas Kouzani

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…

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

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

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) 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…

Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The…

信号处理 · 电气工程与系统科学 2021-11-10 Ronak Aghera , Sahil Chilana , Vishal Garg , Raghunath Reddy

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

A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption profiles of appliances within a residence by analyzing the aggregated consumption signal. Among efficient…

系统与控制 · 电气工程与系统科学 2021-01-19 Elnaz Azizi , Mohammad T H Beheshti , Sadegh Bolouki

Non-Intrusive Load Monitoring (NILM), commonly known as energy disaggregation, aims to estimate the power consumption of individual appliances by analyzing a home's total electricity usage. This method provides a cost-effective alternative…

软件工程 · 计算机科学 2026-02-06 Nazanin Siavash , Armin Moin

We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on…

机器学习 · 计算机科学 2022-11-29 Ruohong Liu , Yize Chen

Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring facilitates user…

机器学习 · 计算机科学 2023-08-01 Jing Xiong , Tianqi Hong , Dongbo Zhao , Yu Zhang

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) 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

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
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