Papers Non-Intrusive Load Monitoring
“Non-Intrusive Load Monitoring” 태그가 달린 논문 99편 · 필터 해제
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
Industrial Non-Intrusive Load Monitoring (NILM) is limited by the scarcity of high-quality datasets and the complex variability of industrial energy consumption patterns. To address data scarcity and privacy issues, we i…
Data AugmentationNon-Intrusive Load MonitoringNon-Intrusive Load Monitoring Based on Image Load Signatures and Continual Learning
Non-Intrusive Load Monitoring (NILM) identifies the operating status and energy consumption of each electrical device in the circuit by analyzing the electrical signals at the bus, which is of great significance for smar…
Continual LearningNon-Intrusive Load MonitoringFew Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, mi…
AllNon-Intrusive Load MonitoringNILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients energy monitoring solutions to provide f…
Non-Intrusive Load MonitoringPrompting Large Language Models for Training-Free Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) aims to disaggregate aggregate household electricity consumption into individual appliance usage and thus enables more effective energy management. While deep learning has advanced NI…
energy managementIn-Context LearningManagementNon-Intrusive Load MonitoringDeep Learning Innovations for Energy Efficiency: Advances in Non-Intrusive Load Monitoring and EV Charging Optimization for a Sustainable Grid
The global energy landscape is undergoing a profound transformation, often referred to as the energy transition, driven by the urgent need to mitigate climate change, reduce greenhouse gas emissions, and ensure sustainab…
Deep Reinforcement LearningNon-Intrusive Load MonitoringEdge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series
The growing global energy demand and the urgent need for sustainability call for innovative ways to boost energy efficiency. While advanced energy-saving systems exist, they often fall short without user engagement. Prov…
Model CompressionNon-Intrusive Load MonitoringTime SeriesA Non-Invasive Load Monitoring Method for Edge Computing Based on MobileNetV3 and Dynamic Time Regulation
In recent years, non-intrusive load monitoring (NILM) technology has attracted much attention in the related research field by virtue of its unique advantage of utilizing single meter data to achieve accurate decompositi…
Dynamic Time WarpingEdge-computingNon-Intrusive Load MonitoringLatent Tensor Factorization with Nonlinear PID Control for Missing Data Recovery in Non-Intrusive Load Monitoring
Non-Intrusive Load Monitoring (NILM) has emerged as a key smart grid technology, identifying electrical device and providing detailed energy consumption data for precise demand response management. Nevertheless, NILM dat…
Computational EfficiencyMissing ValuesNon-Intrusive Load MonitoringEnhancing Non-Intrusive Load Monitoring with Features Extracted by Independent Component Analysis
In this paper, a novel neural network architecture is proposed to address the challenges in energy disaggregation algorithms. These challenges include the limited availability of data and the complexity of disaggregating…
Non-Intrusive Load MonitoringToward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data
Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. T…
Non-Intrusive Load MonitoringPreventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters
Smart grid, through networked smart meters employing the non-intrusive load monitoring (NILM) technique, can considerably discern the usage patterns of residential appliances. However, this technique also incurs privacy …
Adversarial AttackNon-Intrusive Load MonitoringTime Series RegressionBenchmarking Active Learning for NILM
Non-intrusive load monitoring (NILM) focuses on disaggregating total household power consumption into appliance-specific usage. Many advanced NILM methods are based on neural networks that typically require substantial a…
Active LearningBenchmarkingNon-Intrusive Load MonitoringScaled and Inter-token Relation Enhanced Transformer for Sample-restricted Residential NILM
Transformers have demonstrated exceptional performance across various domains due to their self-attention mechanism, which captures complex relationships in data. However, training on smaller datasets poses challenges, a…
Non-Intrusive Load MonitoringRelationTowards a Deeper Understanding of Transformer for Residential Non-intrusive Load Monitoring
Transformer models have demonstrated impressive performance in Non-Intrusive Load Monitoring (NILM) applications in recent years. Despite their success, existing studies have not thoroughly examined the impact of various…
Non-Intrusive Load MonitoringTowards Real-world Deployment of NILM Systems: Challenges and Practices
Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused on developing cloud-exclusive NILM algori…
Non-Intrusive Load MonitoringImproving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and de…
Anomaly DetectionNon-Intrusive Load MonitoringHiFAKES: High-frequency synthetic appliance signatures generator for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) rely on data-driven methods and require extensive datasets of power consumption over a long period of time. However, the scarcity of datasets negatively impacts the performance of cur…
DiversityNon-Intrusive Load MonitoringFederated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data
The importance of Non-Intrusive Load Monitoring (NILM) has been increasingly recognized, given that NILM can enhance energy awareness and provide valuable insights for energy program design. Many existing NILM methods of…
Federated LearningNon-Intrusive Load MonitoringProactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning
Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent sch…
Deep Reinforcement Learningenergy managementManagementNon-Intrusive Load Monitoring