Appliance Classification using BiLSTM Neural Networks and Feature Extraction

Martha T. Correa-Delval, Hongjian Sun, Peter C. Matthews, Jing Jiang

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Number of pages5
Publication statusAccepted/In press - 20 Jul 2021
EventISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies: Smart Grids: Toward a Carbon-free Future - Virtual, Aalto University, Espoo, Finland
Duration: 18 Oct 202121 Oct 2021


ConferenceISGT Europe 2021: IEEE PES Innovative Smart Grid Technologies
Abbreviated titleIEEE PES ISGT EUROPE 2021
Internet address


Dive into the research topics of 'Appliance Classification using BiLSTM Neural Networks and Feature Extraction'. Together they form a unique fingerprint.

Cite this