Appliance Classification using BiLSTM Neural Networks and Feature Extraction

Correa-Delval, Martha T., Sun, Hongjian, Matthews, Peter C. and Jiang, Jing (2021) Appliance Classification using BiLSTM Neural Networks and Feature Extraction. In: 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe): Smart Grids: Toward a Carbon-free Future. IEEE, Piscataway, US, pp. 180-184. ISBN 9781665448765, 9781665448765

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Official URL: 10.1109/ISGTEurope52324.2021.9640...


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.

Item Type: Book Section
Additional Information: ISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies: Smart Grids: Toward a Carbon-free Future; Espoo, Finland; 18-21 Nov 2021
Uncontrolled Keywords: BILSTM, Appliance Classification, NILM
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
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Depositing User: John Coen
Date Deposited: 03 Aug 2021 10:27
Last Modified: 27 Jan 2022 15:15

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