An Improved Sequence-to-Point Learning for Non-Intrusive Load Monitoring Based on Discrete Wavelet Transform

Nonintrusive load monitoring (NILM) is a process that monitors the aggregated power consumption data of customers measured by a single sensor and decomposes the real-time power consumption of each dedicated device. Recent research has been focused on in-depth learning. However, the expensive cost of training time and the huge model scale are not conducive to the realization of smart meters. It is still challenging to accurately predict the real-time power of high-frequency appliances. This paper uses the discrete wavelet transform (DWT) to preprocess the data, which divides the frequency of the training power data. The processed data will be transferred to the enhanced sequence-to-point network (en-S2P) for training. The en-S2P network is the promotion of a sequence-to-point model (S2P) optimized by the autoencoder and bidirectional long short-term memory (Bi-LSTM) layer. And it is compressed by a combined pruning algorithm after being trained. The proposed model method is tested on the UK-DALE and REDD datasets using db2 and sym5 wavelet transform and compared with the S2P. Extensive experiments show that the proposed model can obtain a more satisfactory decomposition performance of appliances, especially high-frequency. In addition, it has a more lightweight scale and maintains a certain degree of sparsity, which is friendly to electricity meters.