Non-Intrusive Load Monitoring

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Most implemented papers archive 2025-07-28

18 shown of 18 papers with code (99 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
  15 Nov 2019 3 repositories listed
  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…
• Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
  25 Jun 2025 1 repository listed
  We show in experiments that NILM models trained with AMDA-augmented data significantly improve the disaggregation of energy consumption of complex industrial appliances like combined heat and power systems.
• Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
  6 Jun 2025 1 repository listed
  In this paper, we introduce CamAL, a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained.
• NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
  6 Jun 2025 1 repository listed
  This paper introduces NILMFormer, a Transformer-based architecture that incorporates a new subsequence stationarization/de-stationarization scheme to mitigate the distribution drift and that uses a novel positional…
• Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data
  28 Jan 2025 1 repository listed
  Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption.
• MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
  27 Jul 2023 1 repository listed
  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
  18 Jul 2023 1 repository listed
  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…
• Challenges in Gaussian Processes for Non Intrusive Load Monitoring
  18 Nov 2022 1 repository listed
  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
  14 Apr 2022 1 repository listed
  We consider the problem of learning the energy disaggregation signals for residential load data.
• ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
  11 Apr 2022 1 repository listed
  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…
• COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
  4 Jun 2021 1 repository listed
  The global effort toward renewable energy and the electrification of energy-intensive sectors have significantly increased the demand for electricity, making energy efficiency a critical focus.
• Energy Disaggregation using Variational Autoencoders
  22 Mar 2021 1 repository listed
  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
  28 Oct 2020 1 repository listed
  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
  2 May 2020 1 repository listed
  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
  20 Jan 2020 1 repository listed
  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 Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
  12 Dec 2019 1 repository listed
  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…
• Transfer Learning for Non-Intrusive Load Monitoring
  23 Feb 2019 1 repository listed
  It is not clear if the method could be generalised or transferred to different domains, e.
• Wavenilm: A causal neural network for power disaggregation from the complex power signal
  23 Feb 2019 1 repository listed
  Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement.