Non-Intrusive Load Monitoring
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Latest papers
MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
jxiong22/matnilm
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Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house.
Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
We also show a 12 percentage point performance advantage of the proposed DL based model over a random forest model and observe performance degradation with the increase of the number of devices in the household, namely with each additional 5 devices, the average performance degrades by approximately 7 percentage points.
Challenges in Gaussian Processes for Non Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances.
Learning Task-Aware Energy Disaggregation: a Federated Approach
ruohliuq/fedmeta
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We consider the problem of learning the energy disaggregation signals for residential load data.
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
ssykiotis/ELECTRIcity_NILM
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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.
COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
arx7ti/cold-nilm
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The modern artificial intelligence techniques show the outstanding performances in the field of Non-Intrusive Load Monitoring (NILM).
Energy Disaggregation using Variational Autoencoders
In this paper we address these issues and propose an energy disaggregation approach based on the variational autoencoders framework.
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.
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.
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.
