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Non-Intrusive Load Monitoring (NILM) identifies the operating status and energy consumption of each electrical device in the circuit by analyzing the electrical signals at the bus, which is of great significance for smart power management.…

Machine Learning · Computer Science 2025-06-10 Olimjon Toirov , Wei Yu

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 method of detecting an individual device's energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy…

Machine Learning · Computer Science 2020-12-23 Mohammad Mahmudur Rahman Khan , Md. Abu Bakr Siddique , Shadman Sakib

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…

Machine Learning · Computer Science 2023-08-01 Jing Xiong , Tianqi Hong , Dongbo Zhao , Yu Zhang

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…

Other Computer Science · Computer Science 2017-03-13 Anthony Faustine , Nerey Henry Mvungi , Shubi Kaijage , Kisangiri Michael

Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on…

Computational Engineering, Finance, and Science · Computer Science 2018-05-16 Alireza Rahimpour , Hairong Qi , David Fugate , Teja Kuruganti

The primary objective of non-intrusive load monitoring (NILM) techniques is to monitor and track power consumption within residential buildings. This is achieved by approximating the consumption of each individual appliance from the…

Signal Processing · Electrical Eng. & Systems 2024-02-29 Sidi Mohammed Kaddour , Mohamed Lehsaini , Abdelhamid Bouchachia

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…

Machine Learning · Computer Science 2021-07-21 Antoine Langevin , Marc-André Carbonneau , Mohamed Cheriet , Ghyslain Gagnon

In recent times, non-intrusive load monitoring (NILM) has emerged as an important tool for distribution-level energy management systems owing to its potential for energy conservation and management. However, load monitoring in smart…

Systems and Control · Electrical Eng. & Systems 2022-06-01 Himanshu Grover , Lokesh Panwar , Ashu Verma , B. K. Panigrahi , T. S. Bhatti

Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This…

Systems and Control · Electrical Eng. & Systems 2023-11-16 Amanie Azzam , Saba Sanami , Amir G. Aghdam

Non-Intrusive Load Monitoring (NILM) aims to estimate appliance-level consumption from aggregate electrical signals recorded at a single measurement point. In recent years, the field has increasingly adopted deep learning approaches;…

Machine Learning · Computer Science 2026-03-06 L. E. Garcia-Marrero , G. Petrone , E. Monmasson

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…

Signal Processing · Electrical Eng. & Systems 2021-11-10 Ronak Aghera , Sahil Chilana , Vishal Garg , Raghunath Reddy

Non-intrusive Appliance Load Monitoring (NALM) aims to recognize individual appliance usage from the main meter without indoor sensors. However, existing systems struggle to balance dataset construction efficiency and event/state…

Signal Processing · Electrical Eng. & Systems 2024-10-23 Zijian Wang , Xingzhou Zhang , Yifan Wang , Xiaohui Peng , Zhiwei Xu

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…

Machine Learning · Computer Science 2025-05-13 Sotirios Athanasoulias

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.…

Signal Processing · Electrical Eng. & Systems 2023-06-09 Mohammad Irani Azad , Roozbeh Rajabi , Abouzar Estebsari

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…

Machine Learning · Computer Science 2021-06-02 Yu Zhang , Guoming Tang , Qianyi Huang , Yi Wang , Hong Xu

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…

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…

Signal Processing · Electrical Eng. & Systems 2024-09-04 Xiangrui Li

Non-intrusive load monitoring (NILM) focuses on disaggregating total household power consumption into appliance-specific usage. Many advanced NILM methods are based on neural networks that typically require substantial amounts of labeled…

Machine Learning · Computer Science 2024-11-26 Dhruv Patel , Ankita Kumari Jain , Haikoo Khandor , Xhitij Choudhary , Nipun Batra

Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients energy monitoring solutions to provide feedback on the…

Machine Learning · Computer Science 2025-06-09 Adrien Petralia , Philippe Charpentier , Youssef Kadhi , Themis Palpanas