Enhancing non-intrusive load monitoring with channel attention guided bi-directional temporal convolutional network for sequence-to-point learning

Abstract

This study addresses the crucial aspect of identifying individual appliance power consumption without extensive sensor deployment, a cornerstone of modern smart grid planning and demand response. Non-Intrusive Load Monitoring (NILM) offers a solution by estimating appliance energy usage from aggregated meter data, increasingly powered by deep learning techniques. However, the depth-dependent effectiveness of load feature extraction can lead to gradient-related issues. To mitigate this, novel approach using a Bi-directional Temporal Convolutional Network (BiTCN) as a residual block foundation, enhanced by a Squeeze-and-Excitation Network (SENet) attention mechanism for channel-wise feature extraction. A departure from conventional methods involves substituting bidirectional non-causal convolution with channel attention for improved feature extraction was introduced. Evaluation on REDD and UK-DALE datasets demonstrates our model’s superiority in load disaggregation compared to existing approaches, emphasizing its potential in advancing NILM through effective deep learning strategies