Non-Intrusive Load Monitoring
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Non-Intrusive Load Monitoring
SynD
Most implemented papers
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
antoniosudoso/attention-nilm
•
•
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances.
Transfer Learning for Non-Intrusive Load Monitoring
It is not clear if the method could be generalised or transferred to different domains, e. g., the test data were drawn from a different country comparing to the training data.
Wavenilm: A causal neural network for power disaggregation from the complex power signal
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement.
On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households.
Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms.
On time series representations for multi-label NILM
Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating.
NILM as a regression versus classification problem: the importance of thresholding
Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load.
Energy Disaggregation using Variational Autoencoders
In this paper we address these issues and propose an energy disaggregation approach based on the variational autoencoders framework.
COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
arx7ti/cold-nilm
•
•
The modern artificial intelligence techniques show the outstanding performances in the field of Non-Intrusive Load Monitoring (NILM).
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
ssykiotis/ELECTRIcity_NILM
•
•
MDPI Sensors 2022
Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal.
