Transfer capabilities of Seq2Seq and Seq2Point CNN architectures in Non-intrusive Load Monitoring with unseen appliances

Luis E. Garcia-Marrero, Giovanni Petrone and Eric Monmasson

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 239, issue C, 211-222

Abstract: In the Non-Intrusive Load Monitoring context, Seq2Seq and Seq2Point Convolutional Neural Network architectures have demonstrated state-of-the-art performance. However, as these methods suffer from high computational costs and the need for large volumes of training data, their transfer capabilities to different domains are essential for real-world implementation. This paper analyzes the drop in performance of Seq2Seq and Seq2Point architectures in the presence of appliances not seen in the aggregated power used for training. A theoretical analysis based on a first-order Taylor expansion is performed to analyze the structure of the additional error incurred. The experimental results showed a significant decrease in the performance of the methods when the noise increases, especially for monitored appliances with low-power states or complex patterns. The study reveals a strong dependence on the aggregated power structure in the training set and suggests that future methods should focus on learning robust appliance-specific signatures rather than directly regressing from the aggregated signal.

Keywords: Non-intrusive load monitoring; Convolutional neural networks; Generalization capabilities; Unseen appliances (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:239:y:2026:i:c:p:211-222

DOI: 10.1016/j.matcom.2025.05.021

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