Sequence-to-point learning with neural networks for nonintrusive load monitoring

Abstract

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.

arXiv comment8 pages, 3 figures

References in corpus (6)

• Sequence to Sequence Learning with Neural Networks
• Pixel Recurrent Neural Networks
• The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
• Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
• SDP Relaxation with Randomized Rounding for Energy Disaggregation
• Interleaved Factorial Non-Homogeneous Hidden Markov Models for Energy Disaggregation

Cited by in corpus (16)

• Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring
• A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem
• On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
• Subtask Gated Networks for Non-Intrusive Load Monitoring
• Non-Intrusive Load Monitoring with Fully Convolutional Networks
• Deep Learning Based Energy Disaggregation and On/Off Detection of Household Appliances
• Structured Dictionary Learning for Energy Disaggregation
• Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring
• A Data-Efficient Approach to Behind-the-Meter Solar Generation Disaggregation
• Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
• A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring
• Machine Learning Systems for Intelligent Services in the IoT: A Survey
• A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks
• Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
• Energy Disaggregation with Semi-supervised Sparse Coding
• PHASED: Phase-Aware Submodularity-Based Energy Disaggregation