A Nonintrusive Load Identification Model Based on Time-Frequency Features Fusion

Abstract

Nonintrusive load monitoring (NILM) plays a key role in the real-time electricity consumption monitoring of household appliances. However, it is difficult to realize high precision load identification by using a single waveform feature. Therefore, this article proposes a two-stream convolutional neural network based on current time-frequency feature fusion for nonintrusive load identification. First, a time series image coding method for current time-frequency multi-feature fusion is proposed. The method can extract the time domain and frequency domain features of the current timing signal effectively. Then, we present a two-stream neural network combining the gated recurrent unit (GRU) and a two-dimensional convolutional neural network (2D-CNN) to improve the load identification performance. Finally, the experimental results on the PLAID and IDOUC datasets show that the proposed model outperforms the state-of-the-art methods.

Keywords

• Nonintrusive load monitoring
• appliance classification
• feature extraction
• signal form