Scaled and Inter-token Relation Enhanced Transformer for Sample-restricted Residential NILM

• URL: http://arxiv.org/abs/2410.12861v2
• Date: Fri, 06 Dec 2024 19:24:54 GMT
• Title: Scaled and Inter-token Relation Enhanced Transformer for Sample-restricted Residential NILM
• Authors: Minhajur Rahman, Yasir Arafat,
• Abstract summary: We propose a novel transformer architecture with two key innovations: inter-token relation enhancement and dynamic temperature tuning.<n>We validate our method on the REDD dataset and show that it outperforms the original transformer and state-of-the-art models by 10-15% in F1 score across various appliance types.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers have demonstrated exceptional performance across various domains due to their self-attention mechanism, which captures complex relationships in data. However, training on smaller datasets poses challenges, as standard attention mechanisms can over-smooth attention scores and overly prioritize intra-token relationships, reducing the capture of meaningful inter-token dependencies critical for tasks like Non-Intrusive Load Monitoring (NILM). To address this, we propose a novel transformer architecture with two key innovations: inter-token relation enhancement and dynamic temperature tuning. The inter-token relation enhancement mechanism removes diagonal entries in the similarity matrix to improve attention focus on inter-token relations. The dynamic temperature tuning mechanism, a learnable parameter, adapts attention sharpness during training, preventing over-smoothing and enhancing sensitivity to token relationships. We validate our method on the REDD dataset and show that it outperforms the original transformer and state-of-the-art models by 10-15\% in F1 score across various appliance types, demonstrating its efficacy for training on smaller datasets.

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