A New Approach for Supervised Power Disaggregation by Using a Denoising Autoencoder and Recurrent LSTM Network
- Resource Type
- Conference
- Authors
- Wang, T. S.; Ji, T. Y.; Li, M. S.
- Source
- 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2019 IEEE 12th International Symposium on. :507-512 Aug, 2019
- Subject
- Aerospace
Bioengineering
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
General Topics for Engineers
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Noise reduction
Hidden Markov models
Neural networks
Training data
Feature extraction
Logic gates
Washing machines
Non-intrusive load monitoring (NILM)
load disaggregation
deep neural network (DNN)
long short-term memory network (LSTM)
denoising autoencoder (dAE)
- Language
Non-intrusive load monitoring (NILM) is a task of estimating the contribution of individual appliance to the overall power consumption by using a set of electrical signals measured by a smart meter. In this paper, we propose a comprehensive and extensible framework based on DNNs. We employ denoising autoencoder (dAE) to reconstruct the power signal of individual appliance from aggregated power consumption, and we use long short term memory (LSTM) network to make sure which appliance the power signal belongs to. We select 5 appliances to validate our method, and the results have shown the advantages of the proposed framework in some aspects compared to hidden Markov models (HMMs) and premier dAE.