Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring
This work addresses energy monitoring in smart buildings, but it appears incremental as it builds on existing deep learning techniques for NILM.
The paper tackled the problem of Non-Intrusive Load Monitoring (NILM) for smart buildings by proposing a Sequence-to-Sequence model with transformer-based attention and temporal pooling, resulting in improved accuracy and computational efficiency compared to state-of-the-art methods.
This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency.