Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Y Himeur, A Alsalemi, F Bensaali… - … Journal of Intelligent …, 2022 - Wiley Online Library
Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …

In situ modeling methodologies in building operation: A review

S Yoon - Building and Environment, 2023 - Elsevier
The building information and operational data generated during the building life cycle are
essential for realizing energy-efficient operation, indoor environmental quality, and …

A novel in-situ sensor calibration method for building thermal systems based on virtual samples and autoencoder

Z Sun, Q Yao, H Jin, Y Xu, W Hang, H Chen, K Li, L Shi… - Energy, 2024 - Elsevier
Sensor networks are playing an increasingly important role in modern buildings. With the
growing size of building sensor networks and the increasing use of low-cost sensors, the …

DeepEdge-NILM: A case study of non-intrusive load monitoring edge device in commercial building

R Gopinath, M Kumar - Energy and Buildings, 2023 - Elsevier
Non-intrusive load monitoring (NILM) has become an emerging technology in the energy
sector for its effectiveness in the energy disaggregation of individual loads from the …

An ensemble-policy non-intrusive load monitoring technique based entirely on deep feature-guided attention mechanism

Z Nie, Y Yang, Q Xu - Energy and Buildings, 2022 - Elsevier
Non-intrusive load monitoring (NILM), as an important part of intelligent electricity
consumption, improves the cognitive level of the load by analyzing the bus power in a …

Multilabel appliance classification with weakly labeled data for non-intrusive load monitoring

G Tanoni, E Principi, S Squartini - IEEE Transactions on Smart …, 2022 - ieeexplore.ieee.org
Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of
the appliances using electrical parameters acquired from a single metering point. State-of …

Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring

A Langevin, M Cheriet, G Gagnon - Sustainable Energy, Grids and …, 2023 - Elsevier
Home energy management systems (HEMS) enable key strategies and methods to improve
residential efficiency and energy utilization. To make informed decisions, HEMS depend on …

Neural Fourier energy disaggregation

C Nalmpantis, N Virtsionis Gkalinikis, D Vrakas - Sensors, 2022 - mdpi.com
Deploying energy disaggregation models in the real-world is a challenging task. These
models are usually deep neural networks and can be costly when running on a server or …

Deep learning-based probabilistic autoencoder for residential energy disaggregation: An adversarial approach

H Çimen, Y Wu, Y Wu, Y Terriche… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Energy disaggregation is the process of disaggregating a household's total energy
consumption into its appliance-level components. One of the limitations of energy …

Torch-nilm: An effective deep learning toolkit for non-intrusive load monitoring in pytorch

N Virtsionis Gkalinikis, C Nalmpantis, D Vrakas - Energies, 2022 - mdpi.com
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. However …