Non-Intrusive Load Monitoring
10 papers with code • 0 benchmarks • 1 datasets
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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.
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 (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.
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.
Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating.
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.
The modern artificial intelligence techniques show the outstanding performances in the field of Non-Intrusive Load Monitoring (NILM).
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.