Results 11 to 20 of about 2,135 (206)

Reliable assessment of uncertainty for appliance recognition in NILM using conformal prediction

Electronics Letters, 2023
A primary task of Non‐intrusive Load Monitoring (NILM) is the identification of appliances that are switched on or off. However, state‐of‐the‐art machine learning methods such as deep learning do not express uncertainty of their predictions.
Lorin Werthen‐Brabants, Tom Dhaene, Dirk Deschrijver   +2 more
doaj   +2 more sources

The 'SmartNIALMeter' electrical appliance disaggregation dataset. [PDF]

Data Brief
Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal.
Vogel M, Friedli M, Camenzind M, Kniesel G, Klemenjak C, Gugolz G, Huber P, Calatroni A, Kaufmann L, Rumsch A, Paice A.   +10 more
europepmc   +2 more sources

Non-Intrusive Load Identification Based on Retrainable Siamese Network. [PDF]

Sensors (Basel)
Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction ...
Lu L, Kang JS, Meng F, Yu M.
europepmc   +2 more sources

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches [PDF]

E3S Web of Conferences, 2022
Reduction and conservation of electrical energy consumption in residential buildings is the main objective of Non-Intrusive Load Monitoring (NILM) techniques.
Ouzine Jamila, Marzouq Manal, Bennani Saad Dosse, Lahreche Khadija, El Fadili Hakim   +4 more
doaj   +1 more source

FedTP-NILM: A Federated Time Pattern-Based Framework for Privacy-Preserving Distributed Non-Intrusive Load Monitoring

Machines
Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources.
Chi Zhang, Biqi Liu, Xuguang Hu, Zhihong Zhang, Zhiyong Ji, Chenghao Zhou   +5 more
doaj   +2 more sources

Comparison between artificial neural network and random forest for effective disaggregation of building cooling load

Case Studies in Thermal Engineering, 2021
Detailed monitoring of loads can provide sufficient information about buildings and help in improving the operation of energy systems. Non-intrusive load monitoring (NILM) has become popular owing to its low cost and lack of disturbance in the occupied ...
Ziwei Xiao, Cheng Fan, Jiaqi Yuan, Xinhua Xu, Wenjie Gang   +4 more
doaj   +1 more source

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Journal of Modern Power Systems and Clean Energy, 2021
Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation. Non-intrusive load monitoring (NILM) offers many promising applications in the context of energy efficiency and ...
Attique Ur Rehman, Tek Tjing Lie, Brice Valles, Shafiqur Rahman Tito   +3 more
doaj   +1 more source

Energy Disaggregation Using Elastic Matching Algorithms [PDF]

, 2020
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article ...
Mporas, Iosif, Paraskevas, Michael, Schirmer, Pascal   +2 more
core   +2 more sources

An Implementation Framework Overview of Non-Intrusive Load Monitoring

Journal of Sustainable Development of Energy, Water and Environment Systems, 2023
The implementation of non-intrusive load monitoring has gained significant attention as a promising solution for disaggregating and identifying individual appliances' energy consumption within households and commercial buildings. The issue at the core of
Omar Al-Khadher, Azharudin Mukhtaruddin, Fakroul Ridzuan Hashim, Muhammad Mokhzaini Azizan, Hussin Mamat   +4 more
doaj   +1 more source

Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances

Sensors, 2022
Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum.
Hafsa Bousbiat, Gerhard Leitner, Wilfried Elmenreich   +2 more
doaj   +1 more source