Graph-Based Dependency-Aware Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is able to analyze and predict users’ power consumption behaviors for further improving the power consumption efficiency of the grid. Neural network-based techniques have been developed for NILM. However, the dependencies of multiple appliances working simultaneously were ignored or implicitly characterized in their models for disaggregation. To improve the performance of NILM, we employ a graph structure to explicitly characterize the temporal dependencies among different appliances. Specially, we consider the prior temporal knowledge between the appliances in the working state, construct a weighted adjacency matrix to represent their dependencies. We also introduce hard dependencies of each appliance to prevent the sparsity of the weighted adjacency matrix. Furthermore, the non-sequential dependencies are learned among appliances using a graph attention network based on the weighted adjacency matrix. An encoder-decoder architecture based on dilated convolutions is developed for power estimation and state detection at the same time. We demonstrate the proposed model on the UKDALE dataset, which outperforms several state-of-the-art results for NILM.