MC-NILM: A Multi-Chain Disaggregation Method for NILM [PDF]
Energies, 2021 Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years.
Hao Ma +4 more
doaj +2 more sources
NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review [PDF]
Energies, 2019 The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage.
Antonio Ruano +4 more
doaj +3 more sources
Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities [PDF]
Scientific ReportsNon-intrusive load monitoring (NILM) is a key way to cost-effectively acquire appliance-level information in advanced metering infrastructure (AMI). Recently, federated learning has enabled NILM to learn from decentralized meter data while preserving ...
Qingquan Luo +5 more
doaj +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 +4 more
doaj +1 more source
Efficient energy consumption in smart buildings using personalized NILM-based recommender system [PDF]
Big Data and Computing Visions, 2021 As the construction sector accounts for the highest energy consumption worldwide, new solutions must be offered in buildings through the adoption of energy-efficient techniques.
Fatemeh Taghvaei, Ramin Safa
doaj +1 more source
Explainable NILM Networks [PDF]
Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, 2020 There has been an explosion in the literature recently on Nonintrusive load monitoring (NILM) approaches based on neural networks and other advanced machine learning methods. However, though these methods provide competitive accuracy, the inner workings of these models is less clear.
Murray, David +2 more
openaire +1 more source
Torch-NILM: An Effective Deep Learning Toolkit for Non-Intrusive Load Monitoring in Pytorch
Energies, 2022 Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics.
Nikolaos Virtsionis Gkalinikis +2 more
doaj +1 more source
IMG-NILM: A Deep learning NILM approach using energy heatmaps
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023 Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load monitoring, NILM (Non-intrusive load monitoring) is low cost, easy to deploy, and flexible.
Jonah Edmonds, Zahraa S. Abdallah
openaire +2 more sources
Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection
Energy Conversion and Economics, 2022 Non‐intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation.
Haijin Wang +5 more
doaj +1 more source
Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond
Energies, 2023 Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of ...
Hafsa Bousbiat +4 more
doaj +1 more source