Dynamic time warping optimization-based non-intrusive load monitoring for multiple household appliances

Non invasive load monitoring (NILM) is beneficial for enhancing the monitoring capability of the distribution network and is crucial for improving the safety of smart grid operation. However, household appliances involve a variety of devices and a large amount of data, making it difficult to achieve high-precision load identification. Especially when a large amount of loads are running simultaneously, problems such as feature overlap and reduced distinguish ability may occur, and thus increasing the difficulty of load identification. To solve these problems, this paper proposes a new NILM method based on dynamic time warping (DTW) optimization and event detection. Firstly, a feature extraction algorithm of STFT-SSAE is constructed by using short-time Fourier transform (STFT) to extract time–frequency features from the load, and then by sparse stack autoencoder (SSAE) to extract important features from time–frequency information. Secondly, the above features are input into Bi-LSTM and DTW models respectively, and a new probabilistic model is established. A Bi-LSTM-DTW load recognition architecture is built by combining the two models. Finally, the load identification model of SSAE-Bi-LSTM based on DTW optimization (DOSL) is trained by the preset combined data, which ensures the high confidence of the DOSL model in various complex operating scenarios. This paper introduces the public data set PLAID for experimental verification. The results show that the selected load features has good discrimination, and the proposed algorithm has the best identification effect with the accuracy rate of 0.9412. The proposed algorithm is also validated for generalization ability on the public dataset UK-DALE, with a identification accuracy rate of 0.9306, and the error of different datasets is only about 1%.