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• W4289823872 abstract "Nonintrusive load monitoring (NILM) techniques allow the individual consumption of devices in an installation to be reported to the user, collaborating in the awareness and managing consumers’ energy use. One of the most critical steps in NILM procedures is feature extraction, which involves the quantitative description of the load signature. Most state-of-the-art methods, particularly those involving voltage–current ( <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>V–I</i> ) trajectories, are based on features that specialists design when manually analyzing signals—the so-called hand-crafted features. On the other hand, this work presents a mathematical method that describes the transient and steady-state (SS) load signature using a 2-D Fourier series, avoiding hand-crafted descriptors. The proposed approach evaluates load identification results with different classifiers and publicly available datasets. In addition to the evaluation of the proposed method’s computational resources, such as execution time and memory footprint in an embedded system, their robustness to noise insertion is also assessed. The results of the proposed approach indicate an average per-class accuracy higher than 90%, equivalent or superior to hand-crafted <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>V–I</i> trajectory-based methods in most cases and even deep learning approaches recently presented in the literature. Such results reinforce the main contributions of this work: 1) a mathematical method to extract interpretable features; 2) results comparable or superior to other state-of-the-art methods; and 3) promising results in computational resources in an embedded system." @default.
• W4289823872 created "2022-08-05" @default.
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• W4289823872 date "2022-09-15" @default.
• W4289823872 modified "2023-10-16" @default.
• W4289823872 title "Feature Extraction of <i>V–I</i> Trajectory Using 2-D Fourier Series for Electrical Load Classification" @default.
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• W4289823872 doi "https://doi.org/10.1109/jsen.2022.3194999" @default.
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