Neural Network-Aided NILM (NNAN) disaggregation: Revealing appliance consumption patterns with iterative subtraction
Non-Intrusive Load Monitoring (NILM) is a method to decompose overall electricity consumption into individual appliance-level data, utilizing the primary meter’s readings without additional sensors on each device. This article introduces a novel approach which is a Neural Network-Aided NILM (NNAN),...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000507 |
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| Summary: | Non-Intrusive Load Monitoring (NILM) is a method to decompose overall electricity consumption into individual appliance-level data, utilizing the primary meter’s readings without additional sensors on each device. This article introduces a novel approach which is a Neural Network-Aided NILM (NNAN), focusing on revealing appliance consumption patterns by following a sequential subtraction method. Our goal is to tackle the issue where high-power and highly-used appliances make it difficult for neural networks to accurately separate the usage of lower-power and less-used appliances. We mainly employ Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) using inception blocks as key components. Our proposed architecture is validated on three public datasets that are AMPds2, ECO and UK-DALE. The NNAN model showed promising results, achieving disaggregation accuracy improvements of up to 5.13% on AMPds2, 3.79% on ECO, and 9.55% on UK-DALE compared to the reference methods. Additionally, NNAN reduces model complexity, requiring up to 74% fewer parameters than traditional deep learning approaches, leading to improved computational efficiency. Finally, NNAN demonstrated a reduced correlation between appliance usage rates and disaggregation accuracies. |
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| ISSN: | 2666-8270 |