Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation
Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang

TL;DR
This paper introduces ELTransformer, an efficient transformer model for energy disaggregation in smart sensor systems, addressing computational complexity and local pattern extraction to improve NILM accuracy.
Contribution
The paper proposes ELTransformer, the first NILM model combining reduced complexity and local pattern modeling using sparse attention and position encodings.
Findings
ELTransformer outperforms state-of-the-art models in accuracy.
It significantly reduces computational complexity.
The model effectively captures local signal patterns.
Abstract
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity usage conditioned on the aggregated power signals (i.e., smart sensor on the main channel). Based on real-time appliance power prediction using sensory technology, energy disaggregation has great potential to increase electricity efficiency and reduce energy expenditure. With the introduction of transformer models, NILM has achieved significant improvements in predicting device power readings. Nevertheless, transformers are less efficient due to O(l^2) complexity w.r.t. sequence length l. Moreover, transformers can fail to capture local signal patterns in sequence-to-point settings due to the lack of inductive bias in local context. In this work, we…
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Taxonomy
TopicsSmart Grid Energy Management · IoT-based Smart Home Systems · Energy Harvesting in Wireless Networks
MethodsRelative Position Encodings
