NILM applications: Literature review of learning approaches, recent developments and challenges
This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
Review of low voltage load forecasting: Methods, applications, and recommendations
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
Adaptive weighted recurrence graphs for appliance recognition in non-intrusive load monitoring
To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load
Monitoring (NILM) research, as researchers and practitioners are forced to invest a …
Monitoring (NILM) research, as researchers and practitioners are forced to invest a …
Toward load identification based on the Hilbert transform and sequence to sequence long short-term memory
S Heo, H Kim - IEEE Transactions on Smart Grid, 2021 - ieeexplore.ieee.org
Load identification is a core concept in non-intrusive load monitoring (NILM). Through NILM
systems, users can check their home appliance usage habits and then adjust their behavior …
systems, users can check their home appliance usage habits and then adjust their behavior …
A critical review of state-of-the-art non-intrusive load monitoring datasets
HK Iqbal, FH Malik, A Muhammad, MA Qureshi… - Electric Power Systems …, 2021 - Elsevier
Abstract Nowadays Non-Intrusive Load Monitoring (NILM) is considered a hot topic among
researchers. The energy disaggregation datasets are used as the benchmark to validate the …
researchers. The energy disaggregation datasets are used as the benchmark to validate the …
Temporal and spectral feature learning with two-stream convolutional neural networks for appliance recognition in NILM
J Chen, X Wang, X Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Non-intrusive load monitoring (NILM) can monitor the operating state and energy
consumption of appliances without deploying sub-meters and is promising to be widely used …
consumption of appliances without deploying sub-meters and is promising to be widely used …
Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks
In many countries distributed energy resources (DER)(eg photovoltaics, batteries, wind
turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic …
turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic …
A residential labeled dataset for smart meter data analytics
Smart meter data is a cornerstone for the realization of next-generation electrical power
grids by enabling the creation of novel energy data-based services like providing …
grids by enabling the creation of novel energy data-based services like providing …
A residual convolutional neural network with multi-block for appliance recognition in non-intrusive load identification
L Qu, Y Kong, M Li, W Dong, F Zhang, H Zou - Energy and Buildings, 2023 - Elsevier
Non-intrusive load monitoring (NILM) is a promising technique for energy consumption
monitoring that can recognize load states and appliance types without relying on excessive …
monitoring that can recognize load states and appliance types without relying on excessive …
Multi-label learning for appliance recognition in NILM using Fryze-current decomposition and convolutional neural network
The advance in energy-sensing and smart-meter technologies have motivated the use of a
Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end …
Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end …