A Non-Intrusive Load Identification Method Based on Multi-Head Attention

Non-intrusive load identification plays a crucial role in developing a green and low-carbon energy supply and demand mechanism. Among the various load identification technologies, the low-frequency electrical signal load identification stands out due to its ability to discern user's electricity consumption habits without increasing the user's hardware cost, thus presenting promising application prospects. However, the challenge lies in the scarcity of available data and the uneven distribution of samples, resulting in reduced accuracy. In this paper, we present a non-intrusive load state identification method based on the combination of LSTM model and multi-head attention model. By fusing those two techniques, the model can extract and utilize richer and more essential intrinsic features in electrical signals. The use of focal loss can balance the weight importance of different states with significantly different sample numbers. The experimental results on two open data-set show that the proposed algorithm can considerably improve the identification accuracy compared to other NILM methods, and the method also has good practical value in real application.