Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring

TL;DR

This paper introduces a sequence-to-point learning framework using bidirectional dilated residual networks for non-intrusive load monitoring, significantly improving appliance power disaggregation accuracy over existing methods.

Contribution

The paper proposes a novel bidirectional dilated residual network architecture for NILM, addressing deep neural network training issues and outperforming state-of-the-art methods.

Findings

Outperforms Seq2point method on REDD and UK-DALE datasets

Achieves higher accuracy across all appliance types

Addresses training difficulties of deep neural networks in NILM

Abstract

Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks to save energy by decomposing corresponding appliances power reading from an aggregate power reading of the whole house. It is a single channel blind source separation problem (SCBSS) and difficult prediction problem because it is unidentifiable. Recent research shows that deep learning has become a growing popularity for NILM problem. The ability of neural networks to extract load features is closely related to its depth. However, deep neural network is difficult to train because of exploding gradient, vanishing gradient and network degradation. To solve these problems, we propose a sequence to point learning framework based on bidirectional (non-casual) dilated convolution for NILM. To be more convincing, we compare our method with the state of art method, Seq2point (Zhang) directly and compare with existing algorithms indirectly via two same datasets and metrics. Experiments based on REDD and UK-DALE data sets show that our proposed approach is far superior to existing approaches in all appliances.