New Time-Frequency Transient Features for Nonintrusive Load Monitoring

Mahfoud Drouaz, Bruno Colicchio, Ali Moukadem, Alain Dieterlen and Djafar Ould-Abdeslam
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Mahfoud Drouaz: IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
Bruno Colicchio: IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
Ali Moukadem: IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
Alain Dieterlen: IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
Djafar Ould-Abdeslam: IRIMAS, Université de Haute-Alsace, 61 rue Albert Camus, 68093 Mulhouse, France

Energies, 2021, vol. 14, issue 5, 1-11

Abstract: A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

Keywords: nonintrusive load monitoring (NILM); time-frequency transform; Stockwell transform; harmonics; feature extraction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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