Papers Non-Intrusive Load Monitoring
“Non-Intrusive Load Monitoring” 태그가 달린 논문 99편 · 필터 해제
Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data
Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV chargin…
energy managementManagementNon-Intrusive Load MonitoringPrediction+1Non-Intrusive Load Monitoring in Smart Grids: A Comprehensive Review
Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding…
ManagementNon-Intrusive Load MonitoringA PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILM
With the growing demand for energy and increased environmental awareness, Non-Intrusive Load Monitoring (NILM) has become an essential tool in smart grid and energy management. By analyzing total power load data, NILM in…
energy managementImputationManagementNon-Intrusive Load Monitoring+1Event Detection for Non-intrusive Load Monitoring using Tukey s Fences
The primary objective of non-intrusive load monitoring (NILM) techniques is to monitor and track power consumption within residential buildings. This is achieved by approximating the consumption of each individual applia…
Event DetectionNon-Intrusive Load MonitoringNon-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection
Understanding electric vehicle (EV) charging on the distribution network is key to effective EV charging management and aiding decarbonization across the energy and transport sectors. Advanced metering infrastructure has…
ManagementNon-Intrusive Load MonitoringLow-Frequency Load Identification using CNN-BiLSTM Attention Mechanism
Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power…
Event DetectionManagementNon-Intrusive Load MonitoringMATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring f…
energy managementNon-Intrusive Load MonitoringEnergy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be direct…
energy managementManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks
Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management. Despite the success of deep models in load disaggregation, t…
Adversarial Attackenergy managementManagementNon-Intrusive Load Monitoring+3Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring
This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improv…
Computational EfficiencyNon-Intrusive Load MonitoringNon-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consu…
Deep LearningManagementNon-Intrusive Load MonitoringEvolutionary Deep Nets for Non-Intrusive Load Monitoring
Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to track electricity consumption of an individual appliance in a household by one aggregated single, such as building level meter readings. The goal …
Deep LearningNon-Intrusive Load MonitoringMSDC: 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. Leveraging recen…
blind source separationNon-Intrusive Load MonitoringEnergy 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. This work propos…
Edge-computingNon-Intrusive Load MonitoringTransfer LearningMulti-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). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale …
Action DetectionActivity DetectionEvent DetectionNon-Intrusive Load MonitoringChallenges in Gaussian Processes for Non Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people sav…
Gaussian ProcessesNon-Intrusive Load MonitoringNon-intrusive Load Monitoring based on Self-supervised Learning
Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load c…
Non-Intrusive Load MonitoringSelf-Supervised LearningZero-Shot LearningRepresentation Learning for Appliance Recognition: A Comparison to Classical Machine Learning
Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Repr…
Information RetrievalNon-Intrusive Load MonitoringRepresentation LearningRetrievalConv-NILM-Net, a causal and multi-appliance model for energy source separation
Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve N…
Non-Intrusive Load MonitoringSpeech SeparationIMG-NILM: A Deep learning NILM approach using energy heatmaps
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 moni…
Deep LearningNon-Intrusive Load MonitoringTime SeriesTime Series Analysis