Non-Intrusive Load Monitoring
14 papers with code • 0 benchmarks • 1 datasets
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SynD
Latest papers with no code
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model
Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind source separation tasks.
Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing
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.
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM).
Non-intrusive Load Monitoring based on Self-supervised Learning
Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.
Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning
On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing.
Conv-NILM-Net, a causal and multi-appliance model for energy source separation
Conv-NILM-net is a causal model for multi appliance source separation.
IMG-NILM: A Deep learning NILM approach using energy heatmaps
IMG-NILM is robust and flexible with consistent performance on various types of appliances; including single and multiple states.
Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components.
Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach
In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model.
DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
