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Transfer Learning for Non-Intrusive Load Monitoring

23 Feb 2019MingjunZhong/transferNILM

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.

NON-INTRUSIVE LOAD MONITORING TRANSFER LEARNING

On time series representations for multi-label NILM

Neural Computing and Applications 2020 ChristoferNal/multi-nilm

Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating.

DIMENSIONALITY REDUCTION NON-INTRUSIVE LOAD MONITORING TIME SERIES

NILM as a regression versus classification problem: the importance of thresholding

28 Oct 2020UCA-Datalab/better_nilm

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.

CLASSIFICATION NON-INTRUSIVE LOAD MONITORING

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

15 Nov 2019antoniosudoso/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.

DENOISING MACHINE TRANSLATION NON-INTRUSIVE LOAD MONITORING SPEECH RECOGNITION TEXT SUMMARIZATION

Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation

20 Jan 2020klemenjak/comparability

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.

NON-INTRUSIVE LOAD MONITORING

On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring

12 Dec 2019klemenjak/nilm-transferability-metrics

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.

NON-INTRUSIVE LOAD MONITORING