Appliance Classification using BiLSTM Neural Networks and Feature Extraction

Correa-Delval, Martha; Sun, Hongjian; Matthews, Peter; Jiang, Jing

doi:10.1109/isgteurope52324.2021.9640061

Appliance Classification using BiLSTM Neural Networks and Feature Extraction

Correa-Delval, Martha; Sun, Hongjian; Matthews, Peter; Jiang, Jing

Authors

Martha Correa Delval martha.t.correa-delval@durham.ac.uk
Post Doctoral Research Associate

Professor Hongjian Sun hongjian.sun@durham.ac.uk
Professor

Dr Peter Matthews p.c.matthews@durham.ac.uk
Associate Professor

Jing Jiang

Abstract

One significant challenge in Non-Intrusive Load Monitoring (NILM) is to identify and classify active appliances used in a building. This research focuses on the classifying process, exploring different approaches for the feature extraction of the appliances’ power load to improve the classification accuracy. In this paper, we present a new method - Spectral Entropy and Instantaneous Frequency-based Bidirectional Long Short Term Memory (SE-IF BiLSTM). It uses feature extraction from the power load to obtain information, such as instant frequency, spectral entropy, spectrogram, Mel spectrogram and signal variation, to feed BiLSTM Neural Network. We also test different options for the BiLSTM to decide the most optimal settings. This method improves the classification performance, achieving up to 98.57% classification accuracy.

Citation

Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. . https://doi.org/10.1109/isgteurope52324.2021.9640061

Conference Name	IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
Conference Location	Espoo, Finland
Start Date	Oct 18, 2021
End Date	Oct 21, 2021
Acceptance Date	Jul 20, 2021
Online Publication Date	Dec 21, 2021
Publication Date	2021
Deposit Date	Jul 20, 2021
Publicly Available Date	Oct 22, 2021
ISBN	9781665448758
DOI	https://doi.org/10.1109/isgteurope52324.2021.9640061

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