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Non-Intrusive Appliance Identification with Appliance-Specific Networks
- Source :
- IAS
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. We adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.
- Subjects :
- Computer science
020209 energy
02 engineering and technology
010501 environmental sciences
Overfitting
Machine learning
computer.software_genre
01 natural sciences
Blind signal separation
Industrial and Manufacturing Engineering
0202 electrical engineering, electronic engineering, information engineering
Oversampling
Electrical and Electronic Engineering
Hidden Markov model
0105 earth and related environmental sciences
Artificial neural network
Event (computing)
business.industry
Deep learning
Identification (information)
Control and Systems Engineering
Unsupervised learning
Data mining
Artificial intelligence
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE Industry Applications Society Annual Meeting
- Accession number :
- edsair.doi.dedup.....76b03fde7e8886b6cbdb3e4e9d7ed775