Resumen
Most current non-intrusive load monitoring methods focus on traditional load characteristic analysis and algorithm optimization, lack knowledge of users? electricity consumption behavior habits, and have poor accuracy. We propose a novel attention-guided bidirectional dynamic graph IndRNN approach. The method first extends sequence or multidimensional data to a topological graph structure. It effectively utilizes the global context by following an adaptive graph topology derived from each set of data content. Then, the bidirectional Graph IndRNN network (Graph IndRNN) encodes the aggregated signals into different graph nodes, which use node information transfer and aggregation based on the entropy measure, power attribute characteristics, and the time-related structural characteristics of the corresponding device signals. The function dynamically incorporates local and global contextual interactions from positive and negative directions to learn the neighboring node information for non-intrusive load decomposition. In addition, using the sequential attention mechanism as a guide while eliminating redundant information facilitates flexible reasoning and establishes good vertex relationships. Finally, we conducted experimental evaluations on multiple open source data, proving that the method has good robustness and accuracy.