Non-Intrusive Load Monitoring

10 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

antoniosudoso/attention-nilm 15 Nov 2019

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

MingjunZhong/transferNILM 23 Feb 2019

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

picagrad/WaveNILM 23 Feb 2019

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

klemenjak/nilm-transferability-metrics 12 Dec 2019

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

klemenjak/comparability 20 Jan 2020

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

ChristoferNal/multi-nilm Neural Computing and Applications 2020

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

UCA-Datalab/better_nilm 28 Oct 2020

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

ETSSmartRes/VAE-NILM 22 Mar 2021

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 4 Jun 2021

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.