Load Energy Decomposition Algorithm Based on Improved Bidirectional Transformer Combined With Time-Sensing Self-Attention

Abstract

Non-Intrusive Load Monitoring (NILM) represents a technique for energy monitoring that is adept at discerning the quantity, type, and operational status of appliances through the analysis of aggregate power data within a specified area. This method facilitates the disaggregation of total energy consumption into the power usage of individual appliances. Recent advancements in deep learning have led to the application of various neural networks in the context of NILM. However, the presence of multiple appliances operating concurrently in various states, coupled with significant disparities in appliance switching behavior, poses challenges to the efficacy of disaggregation models. To mitigate these challenges, this study introduces a novel load-energy disaggregation algorithm, dubbed GTA-BT, which integrates Gated Recurrent Units (GRU) and a bidirectional Transformer architecture featuring time-aware self-attention. This approach aims to enhance disaggregation accuracy for appliances that exhibit multi-stage operational patterns and low utilization rates. The bidirectional Transformer is employed to capture local feature dependencies across different segments of the power consumption sequence, while the integration of GRU facilitates effective modeling of temporal sequences. In alignment with NILM task requirements, this model incorporates a device time state variable within the self-attention mechanism to augment focus on active appliances and extends the local receptive field through convolutional layer expansion. Moreover, a novel masking strategy, predicated on the operational state of devices, has been developed to heighten the model’s responsiveness to transitions in multi-stage appliance states. Comparative evaluations of this method against leading NILM algorithms using the REDD and UK-DALE datasets demonstrate its superior performance, evidenced by a 25% reduction in Mean Absolute Error (MAE) across all appliances, a 20% enhancement in F1-Score, and overall improved disaggregation capability.

Keywords

• Bidirectional transformer
• dilated convolution
• dynamic mask strategy
• energy decomposition
• GRU
• gated time-aware self-attention