Appliance Classification using BiLSTM Neural Networks and Feature Extraction

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

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