Non-Intrusive Load Disaggregation Based on Residual Gated Network

Abstract

Non-intrusive load disaggregation is designed to estimate the power consumption of each appliance based on the total power of the appliance in the household. Conventional machine learning algorithms cannot accurately extract semantic information from time series data, which motivates us to implement nonintrusive load disaggregation using residual gated recurrent neural networks model (Res-GRU). First, the networks model use multi-scale convolution kernels networks model extract time series data features, and will get multiple map fusions. Secondly, the networks model use residual learning to deepen the network to extract deep load features. Finally, the networks model use the gated recurrent unit to reset and update high level features. In this way, we can get the output power value of the target appliance. The experimental results show that the proposed network model has a good disaggregation effect.