Non-Intrusive Load Monitoring
14 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data
To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data.
Non-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.
Non-Intrusive Load Monitoring with Missing Data Imputation Based on Tensor Decomposition
With the widespread adoption of Non-Intrusive Load Monitoring (NILM) in building energy management, ensuring the high quality of NILM data has become imperative.
Event 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.
Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection
Our experimental results demonstrate high-accuracy EV charging detection at the feeder level, achieving an F-Score of 98. 88% in offline detection and 93. 01% in online detection.
Low-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.
On 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.
Sequence-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.
Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms.
Evolutionary Deep Nets for Non-Intrusive Load Monitoring
The goal of NILM is to disaggregate the appliance from the aggregated singles by computational method.
