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Appliance Classification using BiLSTM Neural Networks and Feature Extraction

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

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Jing Jiang


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.


Correa-Delval, M., Sun, H., Matthews, P., & Jiang, J. (2021). Appliance Classification using BiLSTM Neural Networks and Feature Extraction. .

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


Accepted Conference Proceeding (330 Kb)

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